{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "960ceacb-3c3f-484c-95b9-172bd009b1a4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a generic code for drawing a state's Home Districts for neutral map estimation\n",
      "In this code, we use the term 'tract' generically to refer the base building block, usually vtd's\n"
     ]
    }
   ],
   "source": [
    "#  THIS IS THE PRIMARY VERSION TO USE FOR ALL STATES  #See MO for orig 2/10/24.  This one from GA 3-20-24, methods from IN\n",
    "print(\"This is a generic code for drawing a state's Home Districts for neutral map estimation\") #Jan'24\n",
    "print(\"In this code, we use the term 'tract' generically to refer the base building block, usually vtd's\")\n",
    "import shapely\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "from shapely.ops import nearest_points, transform\n",
    "from shapely.affinity import translate, scale\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "from scipy.optimize import minimize, minimize_scalar\n",
    "import math\n",
    "import time\n",
    "import ast\n",
    "# from HDmethods import *   #I want to implement this, but my HDmethods require shapely functions\n",
    "# see https://stackoverflow.com/questions/66877728/proper-way-to-use-import-when-calling-external-function for a future workaround\n",
    "\n",
    "dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly,LW=1):\n",
    "    dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.geom_type == dummyPoly.geom_type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y,lw=LW)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            if geom.area > 0:  #to avoid error with LineString geom parts\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y,lw=LW)         \n",
    "def plotCenter(t,geom,FONTSIZE=10):\n",
    "    plt.text(geom.centroid.x,geom.centroid.y,t,ha='center',fontsize=FONTSIZE)\n",
    "    \n",
    "def r3(number):\n",
    "    result = round(number,3)\n",
    "    return result\n",
    "\n",
    "def r5(number):\n",
    "    result = round(number,5)\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ab4a3d5-f4e7-4aec-b81c-0e786cd35b57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def getWeightedAvgAndSD(LIST, WEIGHTS):  #from IN\n",
    "    nWeights = len(WEIGHTS)\n",
    "    normWeights = [WEIGHTS[i] / np.sum(WEIGHTS) for i in range(nWeights) ]\n",
    "    AVG = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        AVG += normWeights[i] * value\n",
    "    sumVar = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        sumVar += normWeights[i] * (value - AVG)**2\n",
    "    SD = sumVar ** 0.5     #don't need to normalize again since weights were normalized\n",
    "    return AVG, SD\n",
    "\n",
    "def squish(shapeList, cutLINE, farLINE, newFarLINE):\n",
    "    \"\"\"\n",
    "    This method compresses a list of shapes lying between the cutLINE and the farLINE)\n",
    "    such that the Polygons now lie between the cutLINE and the newFarLINE, which is closer to the cutLINE\n",
    "    Used to compress state outcroppings into a more compact state representation.\n",
    "     (I tried doing this point by point (see #'s), but this overdistorted geometries.)\n",
    "      (#This was a manual alternative to shapely.affinity.scale and .translate operations)\n",
    "       So instead, we run this AFTER established untransformed map topology, and \n",
    "        we simply scale and translate each unit.  This loses true connectivity.\n",
    "        Scaling is all lengths scale by compression of distance\n",
    "    The cut, far, and newFar LineString segments are preferably parallel\n",
    "    The method returns the modified shapes of all pollys\n",
    "    \"\"\"\n",
    "    if cutLINE.intersects(farLINE) or cutLINE.intersects(newFarLINE):\n",
    "        print(\"cutLine,farLine, newFarLine were\",cutLINE, farLINE, newFarLINE)\n",
    "        raise Exception(\"ERROR: the proposed cutLine intersects the current or proposed far line\")\n",
    "    newPollys = list()\n",
    "    for polly in shapeList:\n",
    "        pollyCP = polly.centroid\n",
    "        cutPt =     nearest_points(cutLINE,   pollyCP)[0]\n",
    "        farPt =     nearest_points(farLINE,   pollyCP)[0]\n",
    "        newFarPt =  nearest_points(newFarLINE,pollyCP)[0]\n",
    "        curr_cutX, curr_cutY =            pollyCP.x - cutPt.x, pollyCP.y -  cutPt.y\n",
    "        currFar_CutX , currFar_CutY  =    farPt.x - cutPt.x, farPt.y   -  cutPt.y\n",
    "        new_currFarX, new_currFarY =      newFarPt.x - farPt.x, newFarPt.y - farPt.y\n",
    "        newFar_CutX,  newFar_CutY =       newFarPt.x - cutPt.x, newFarPt.y   -  cutPt.y\n",
    "        distanceRatio = newFarPt.distance(cutPt)/farPt.distance(cutPt) #scale area ^length^2\n",
    "        XOFF,YOFF = 0., 0.\n",
    "        XFACT, YFACT = 1., 1.  #default = no stretch\n",
    "        # basic equation: (new-cut)/(curr-cut) = (newFar-cut)/(currFar - cut)\n",
    "        #  or: (new-curr)  = (curr-cut)*(newFar-currFar)/(currFar - cut)   # -1 to both sides\n",
    "        if currFar_CutX != 0:\n",
    "            XOFF =  curr_cutX * new_currFarX / currFar_CutX\n",
    "            XFACT =              newFar_CutX / currFar_CutX\n",
    "        if currFar_CutY != 0:\n",
    "            YOFF =  curr_cutY * new_currFarY / currFar_CutY\n",
    "            YFACT =              newFar_CutY / currFar_CutY\n",
    "        newPolly = translate(polly,xoff=XOFF,yoff=YOFF)\n",
    "        newPolly = scale(newPolly, xfact=XFACT, yfact=YFACT, origin='centroid')\n",
    "        newPollys.append( newPolly )       \n",
    "    return newPollys\n",
    "\n",
    "def getHDcp(TRACTCP,TRACTPOP, TRACTLIST, SPLITTRACTNO = -777,SPLITTRACTUSE = 1.):  #population centerpoint of a Home District or county (cluster)\n",
    "    cpx, cpy, sumPop = 0.,0., 0.\n",
    "    for tt in TRACTLIST:\n",
    "        USE = 1.\n",
    "        if tt ==    SPLITTRACTNO:\n",
    "            USE =   SPLITTRACTUSE\n",
    "        sumPop += USE*TRACTPOP[tt]\n",
    "        cpx +=    USE*TRACTPOP[tt] * TRACTCP[tt].x\n",
    "        cpy +=    USE*TRACTPOP[tt] * TRACTCP[tt].y\n",
    "    HDCP_ = Point(cpx/sumPop, cpy/sumPop)\n",
    "    return HDCP_\n",
    "\n",
    "#  The below four methods are TO SNAP to HD SHAPES without any solving / iteration\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    The optional xScale parameter is the ratio of longitude to latitude lengths scales (<<1 for far from equator)\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def buildPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a 4-tri polygon from four HD wedges emanating from the centerpoint\n",
    "    Pt = [Point(0,0)]*12                      #xScale has same meaning as in buildWedge\n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW]  #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ]  \n",
    "        Pt[nW*2] =   Point(CP.x + RADII[nW]/XSCALE*math.cos(ccwAngle), CP.y + RADII[nW]*math.sin(ccwAngle) )\n",
    "        Pt[nW*2+1] = Point(CP.x + RADII[nW]/XSCALE*math.cos( cwAngle), CP.y + RADII[nW]*math.sin( cwAngle) )        \n",
    "    POLLY = Polygon([Pt[0],Pt[1],Pt[2],Pt[3],Pt[4],Pt[5],Pt[6],Pt[7] ])\n",
    "    return POLLY\n",
    "\n",
    "def buildArcPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a fuller polygon from four HD wedges emanating from the centerpoint\n",
    "    twoPi = 2. * 3.1415926   #xScale has same meaning as in buildWedge\n",
    "    POINTS = list()                   \n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW] % twoPi                 #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ] % twoPi \n",
    "        midAngle = [0.75*ccwAngle + 0.25*cwAngle , 0.25*ccwAngle + 0.75*cwAngle ]  #avoid s sampling cone center for wide angles\n",
    "        if abs (ccwAngle - cwAngle) > 3.1415926 :  #angles straddle angle=0\n",
    "            midAngle = [0.75*(ccwAngle - twoPi) + 0.25*cwAngle , 0.25*(ccwAngle -twoPi) + 0.75*cwAngle ]\n",
    "        angList = [ccwAngle] + midAngle + [cwAngle]\n",
    "        for ang in angList:\n",
    "            POINTS.append(Point(CP.x + RADII[nW]/XSCALE*math.cos(ang), CP.y + RADII[nW]*math.sin(ang) ) )\n",
    "                          \n",
    "    POLLY = Polygon(POINTS)\n",
    "    return POLLY\n",
    "\n",
    "def getPolyPop(POLLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points contained by a polygon\n",
    "    WPOP, WLIST = 0., list()\n",
    "    for tt in CANDIDATELIST:\n",
    "        if POLLY.contains(TRACTCP[tt]):\n",
    "            WPOP += TRACTPOP[tt]\n",
    "            WLIST.append(tt)\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonCPP(POLLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county in getPolyPop\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if POLLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonHCunits(POLLY, HC, UNITLIST, UNITCP, UNITPOP, ALLFUSEDCOUNTIES, AREAFRAC, COUNTYGEOM, \n",
    "                  NEIGHBORCOUNTYLIST, COUNTYUNITLIST):\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"unit counties\" are added as whole units if a sufficient area fraction is captured.  For non-unitCs, we probe each component vtd / tract\n",
    "    \"\"\"\n",
    "    UPOP, ULIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county before we called this method\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        #1/9/24 - NEED TO MODIFY THE ORDER BELOW TO AVOID CREATING HOLES AND ENCLAVES ########################\n",
    "        \n",
    "        for c in latestClist:\n",
    "            for cc in list( set(NEIGHBORCOUNTYLIST[c]).difference(set(ALLFUSEDCOUNTIES)) ):\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        if cc+0.5 in UNITLIST:\n",
    "                            if POLLY.intersection(COUNTYGEOM[cc]).area >=  AREAFRAC[cc] * COUNTYGEOM[cc].area :\n",
    "                                intersectedClist.append(cc)   #for unit counties, intersections only count if they capture sufficient area\n",
    "                                newClist.append(cc)\n",
    "                                UPOP += UNITPOP[UNITLIST.index(cc+0.5)]\n",
    "                                ULIST.append( UNITLIST.index(cc+0.5) )\n",
    "                        else:                           \n",
    "                            intersectedClist.append(cc)\n",
    "                            newClist.append(cc)\n",
    "                            if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                                unitsToAdd = COUNTYUNITLIST[cc]\n",
    "                                UPOP += np.sum(  [ UNITPOP[uu] for uu in unitsToAdd ]  )\n",
    "                                ULIST += unitsToAdd\n",
    "                            else:\n",
    "                                for uu in COUNTYUNITLIST[cc]:\n",
    "                                    if POLLY.contains(UNITCP[uu]):\n",
    "                                        UPOP += UNITPOP[uu]\n",
    "                                        ULIST.append(uu)\n",
    "        latestClist = newClist.copy()\n",
    "        \n",
    "    return UPOP, ULIST\n",
    "\n",
    "def clusterSwell(CP_, CCBgeom, CCBpop, allUnits, unitCP, unitPop, unitNbrs, TGTPOP):\n",
    "    \"\"\"\n",
    "    This method grows a Home District by \"swelling\" from a corner county cluster.  First, we add the full cluster,\n",
    "     then we add closest neighbor units to the home district centerpoint CP_ until we reach the TGTPOP\n",
    "     \"\"\"\n",
    "    #global borderUnits\n",
    "    hd_CCBdist = [CP_.distance(geo) for geo in CCBgeom]\n",
    "    CCBno = hd_CCBdist.index(np.min(hd_CCBdist))  #ID's the closest cluster to this HD center.  Should contain the HD, but rarely will not\n",
    "    unitNo = allUnits.index(CCBno+0.25)\n",
    "    newList, prevList, addedList, addedPop = [unitNo], [unitNo], [unitNo], unitPop[unitNo]  #seed with the cluster pop and unit\n",
    "    while addedPop < 0.99 * TGTPOP and len(newList) > 0:\n",
    "        trySet = set(list())\n",
    "        for U in addedList:\n",
    "            trySet = trySet.union(set(unitNbrs[U] ) )\n",
    "        tryList = list( trySet.difference(set(addedList)) )\n",
    "        tryDist = [ CP_.distance(unitCP[UU]) for UU in tryList ]\n",
    "        idx = np.argsort(tryDist)\n",
    "        idxNo, newList = 0, list()\n",
    "        haveNotAdded = True\n",
    "        while idxNo < len(tryList) and haveNotAdded:\n",
    "            UU = tryList[idx[idxNo]]\n",
    "            if unitPop[UU] + addedPop < 1.01 * TGTPOP and wontEnclave(UU, addedList, unitNbrs, borderUnits) :\n",
    "                newList.append(UU)\n",
    "                addedList.append(UU)\n",
    "                addedPop += unitPop[UU]\n",
    "                haveNotAdded = False\n",
    "            idxNo +=1\n",
    "        prevList = newList.copy()\n",
    "        \n",
    "    return addedPop, addedList\n",
    "            \n",
    "def getFakeHC(HDPOLLY, HC, CCBlist, countyGeom, MAP) :\n",
    "    \"\"\"\n",
    "    This method is for setting a fake home county for counties in corner clusters, so that county neighbors can be found budding from the \"home county\"\n",
    "    We pick the county in the cluster closest to the map center and intersecting the HDpoly.  This will be an active county on the cluster boundary   \n",
    "    \"\"\"\n",
    "    MAPcenter = MAP.centroid\n",
    "    for L in CCBlist:\n",
    "        if HC in L:\n",
    "            distList, cList = list(), list()\n",
    "            for C in L:\n",
    "                if HDPOLLY.intersects(countyGeom[C]):\n",
    "                    distList.append(countyGeom[C].distance(MAPcenter))\n",
    "                    cList.append(C)\n",
    "    fakeHC = cList[distList.index(np.min(distList)) ]\n",
    "    return fakeHC\n",
    "\n",
    "def getLongDist(CP1, CP2, xScale = 1.0): #distance between two points with EW distance scaled down by latitude\n",
    "    #LAT = MAP.centroid.y\n",
    "    #xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "    dist = ( xScale*xScale * (CP1.x - CP2.x)*(CP1.x - CP2.x)  +  (CP1.y - CP2.y)*(CP1.y - CP2.y) ) **0.5 \n",
    "    return dist\n",
    "\n",
    "# THESE ARE THE METHODS USED IN SOLVING HD SHAPES\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def getCountyWP(T, WEDGEPOLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points in a wedge excluding a point\n",
    "        WPOP, WLIST = 0., list()\n",
    "        for tt in CANDIDATELIST:\n",
    "            if tt != T and WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                WPOP += TRACTPOP[tt]\n",
    "                WLIST.append(tt)\n",
    "        return WPOP, WLIST\n",
    "\n",
    "def getNonCWP(WEDGEPOLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by an infinite polygonal wedge for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def isIncludedA(STARTa, ENDa, TESTa): #determines if a test angle is between a start and end angle (True)\n",
    "    \"\"\"\n",
    "    The start angle must be less than the end angle when placed on the [0, 2 pi] interval  (ccw convention)\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    STARTa, ENDa, TESTa = STARTa % (2.*pi), ENDa % (2.*pi), TESTa % (2.*pi)  #place on the 2pi interval\n",
    "    isIncludedA = False\n",
    "    if STARTa  > ENDa :  #included angle straddles east\n",
    "        if   TESTa  >= STARTa or TESTa < ENDa :  #near-east between the two\n",
    "            isIncludedA = True\n",
    "    else:\n",
    "        if TESTa >= STARTa and TESTa < ENDa :  #normal case\n",
    "            isIncludedA = True\n",
    "    return isIncludedA\n",
    "\n",
    "def getNonCWP_c(STARTANGL, ENDANGL, HC, TRACTCP,TRACTPOP,COUNTYGEOM,COUNTYPOP,COUNTYTRACTLIST,\n",
    "                                    NEIGHBORCOUNTYLIST, UUDIST, UUANGLE, WEDGEPOLY) :\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a polygonal wedge for counties contiguous with the Home County (no hop-overs),\n",
    "    EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    Unlike its getNonCWP vanilla parent, this one uses angles rather than infinite wedges for units (still wedges for counties)\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if isIncludedA(STARTANGL, ENDANGL, UUANGLE[tt]): #WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getExitAngle(CP,WEDGEPOLY, MAP, A0, A1):\n",
    "    \"\"\"\n",
    "    This code determines the orientation angle from a CenterPoint to the intersection of a wedge with an exterior boundary\n",
    "    If an intersection is not found, we just take the average angle of the wedge start/stop angles A0, A1 as this orientation angle\n",
    "    MAP must be a single polygon for this to work in the revised code (tho could run thru all polygons in MAP if a multipolygon)\n",
    "    \"\"\"\n",
    "    EXITANGLE = 0.5* (A0 + A1) #this will be the angle from the x-axis to the exit beeline\n",
    "    if WEDGEPOLY.intersects(MAP.exterior):\n",
    "        edgeLine = WEDGEPOLY.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "        closePoint = nearest_points(edgeLine,CP)[0]\n",
    "        dx = closePoint.x - CP.x       #this and below lines were indented in original code***\n",
    "        dy = closePoint.y - CP.y\n",
    "        EXITANGLE =  pi/2. * np.sign(dy)  #default in case dx=0\n",
    "        if (dx != 0. ):\n",
    "            EXITANGLE = math.atan(dy/dx) + random.uniform(-0.01,0.01) #add wiggle to avoid exact NESW orientation in gridded states\n",
    "            if dx < 0. :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                EXITANGLE = pi + EXITANGLE\n",
    "    return EXITANGLE # this reorients 0th wedge to face boundary's closest point\n",
    "\n",
    "def getNewAngles(mWP, tWP, ADP, LEVEL_L, MINADJRATIO, MAXANGLE, MAXANGLERATIO, nUNFILLEDWEDGES): \n",
    "    \"\"\"\n",
    "    This method classifies Home Districts based on how their post-reoriented equi-angle max wedge pops stack up vs targets,\n",
    "     then changes wedge angles in some situations to pick up more population along the boundary\n",
    "    If there is one wedge that will fall short of a quarter district pop, then we adjust angles if it is sufficiently short    \n",
    "    (Using \"HD2\" code, the opposite wedge's pop will be constrained as well.)\n",
    "    If there are two opposite-facing constrained wedges, we do not adjust angles\n",
    "    If there are two adjacent constrained wedges, we classify as a corner (no angle change) if the adjacent wedge is sufficiently constrained,\n",
    "      otherwise we treat as a single constrained wedge\n",
    "    If there are three constrained wedges, the angles are unchanged; we use the 4th wedge to pick up all pop\n",
    "    If all four wedges are constrained, there is an error and we raise a flag.\n",
    "    mWP is the quadlist of maxWedgePops we would get by extending each wedge to the MAP boundary\n",
    "    tWP is the quadlist of targetWedgePops\n",
    "    LEVEL_L is the non-dimensional distance to the boundary at which we no longer adjust wedge angles to drive more near-boundary pop\n",
    "    MAXANGLERATIO is the max ratio of the wide angle (facing the boundary) to the normal angle (e.g. 90deg for four wedges) ...\n",
    "    but this is truncated near-boundary to MAXANGLE (e.g. 1.8 pi/2 instead of increasing all the way to 1.9 pi/2)\n",
    "      NOTE THAT this code does not consider nearly-shorted wedges -- those are a separate method called later if all mWP's > tWP's\n",
    "    \"\"\"   \n",
    "    isChange = False   #default; we are NOT changing angles\n",
    "    printDebuggg = False\n",
    "    NWEDGES = len(mWP)\n",
    "    avgWedgeAngle = 2.*math.pi / NWEDGES\n",
    "    minW = mWP.index(np.min(mWP))  #index of the wedge with the lowest max wedge pop\n",
    "    oppW = int( int(minW + NWEDGES/2) % NWEDGES )  #and its opposing wedge\n",
    "    Lstar = ( mWP[minW] / (0.25*ADP) )**0.5  #shortest wedge's nondim'l distance to boundary\n",
    "    \n",
    "    case = \"keep same wedge angles\" #default; no unfillable wedges or all but one are unfillable\n",
    "    if nUNFILLEDWEDGES >= NWEDGES:\n",
    "        raise Exception(\"ERROR! ALL WEDGES ARE CONSTRAINED for tract\",t,\".  IMPOSSIBLE!!\")\n",
    "    if nUNFILLEDWEDGES == 1:\n",
    "        case = \"constrain opp wedge\"\n",
    "        if Lstar >= levelL :\n",
    "            case = \"keep same wedge angles\"  #not close enough to boundary to distort HD shape\n",
    "    if nUNFILLEDWEDGES == 0 or nUNFILLEDWEDGES == NWEDGES - 1:  #far from boundary or with only one wedge w/large pop\n",
    "        case = \"keep same wedge angles\"   \n",
    "    if nUNFILLEDWEDGES == 2:  #must determine if opposite or adjacent           \n",
    "        if mWP[oppW] < tWP[oppW]:  #shorted wedges oppose each other, so ...\n",
    "            case = \"keep same wedge angles\" #...the HD shape will naturally widen to pick up adjacent pop\n",
    "        else:  #we have 2 adjacent (non-opposing) shorted wedges... but how shorted?  ID the adjacent wedge\n",
    "            for nW in range(NWEDGES):\n",
    "                if mWP[nW] < tWP[nW] and nW != minW :\n",
    "                    adjW = nW  #this is the adjacent wedge\n",
    "                    adjRatio = mWP[adjW] / tWP[adjW]\n",
    "            if adjRatio < minAdjRatio:  #we're close to a corner (this adjacentWedge is significantly constrained)\n",
    "                case = \"keep same wedge angles\"\n",
    "                if printDebuggg :\n",
    "                    print(\"Not adjusting wedge angles for tract\",t,\"as 2nd-sparsest wedge\",adjW,\"has pop\",mWP[adjW] )\n",
    "            else:\n",
    "                case = \"constrain opp wedge\"  #we will set the angles and target pops as if the adj wedge were not constrained\n",
    "    \n",
    "    if case == \"keep same wedge angles\":\n",
    "        isChange = False\n",
    "        WEDGEANGLE = [avgWedgeAngle for w in range(NWEDGES) ]\n",
    "\n",
    "    if case == \"constrain opp wedge\":  #Here, we modify wedge angles.  MWP's will be recalc'd outside the method\n",
    "        isChange = True  \n",
    "        wideAngle = min(MAXANGLE, avgWedgeAngle*max(  1,( 1.+(MAXANGLERATIO-1.)*(LEVEL_L - Lstar)/(LEVEL_L - 0.) )  ) )\n",
    "        WEDGEANGLE = [(2.*pi - 2. * wideAngle)/ (NWEDGES - 2.) for w in range(NWEDGES) ]  \n",
    "        WEDGEANGLE[minW] = wideAngle\n",
    "        WEDGEANGLE[oppW] = wideAngle\n",
    "    \n",
    "    return isChange, WEDGEANGLE\n",
    "\n",
    "def rebalanceTWPs(MWP, TWP, BARREDLIST=list()):\n",
    "    \"\"\"\n",
    "    This method iteratively increases targetWedgePops to accommodate wedges that have maxWedgePop < targetWedgePop.\n",
    "    The wedgePop gap is distributed equally among wedges that have some capacity and are not BARRED (e.g. an opposite wedge in HD2 method)\n",
    "     (If this pushes some wedges over capacity, this will be fixed in a later run through loop inside this method)\n",
    "    We also flag which wedges \"will fill\" = have sufficient capacity after the rebalancing of the targets to not use the entire maxWedgePoly\n",
    "    \"\"\"\n",
    "    DEBUGrTWP = False\n",
    "    NWEDGES = len(MWP)\n",
    "    unorderedCapacity = [MWP[w] - TWP[w] for w in range(NWEDGES) ]\n",
    "    idx = np.argsort(unorderedCapacity)\n",
    "    #for nn in range(NWEDGES):\n",
    "    #    print(MWP[idx[nn]])\n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        for nn in range(NWEDGES):\n",
    "            print(\"barred list, orig MWP, TWP\",BARREDLIST, r3(MWP[nn]),r3(TWP[nn]) )\n",
    "    \n",
    "    for nn in range(NWEDGES):  #going from least to most maxWedgePop here ...\n",
    "        nW = idx[nn]\n",
    "        if MWP[nW] < TWP[nW]: #need to redistribute extra target to higher-capacity wedges ...\n",
    "            wedgePopGap = TWP[nW] - MWP[nW]\n",
    "            TWP[nW] = MWP[nW]  #max out this wedge\n",
    "            nReceivers = 0.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....\n",
    "                    nReceivers += 1.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....                    \n",
    "                    TWP[WW] += wedgePopGap / nReceivers  #... so give it equal share of the gap, even if this goes over; we'll correct in later loop\n",
    "                    \n",
    "    WILLFILL = [1]*NWEDGES\n",
    "    for nW in range(NWEDGES):\n",
    "        if MWP[nW] < 1.001* TWP[nW]:  #required pop nearly or truly requires the entire wedge\n",
    "            WILLFILL[nW] = 0 \n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        print(\"adjusted TWP's are\",TWP)\n",
    "    return TWP, WILLFILL\n",
    "\n",
    "def solveWedge(nontractTWP,t, hC, STARTANGLE, ENDANGLE, MAXD, TOLERPOPS, tractCP, tractPop,\n",
    "               countyTractList, countyGeom, countyPop, neighborCountyLIST,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    #### NO LONGER USED; SEE FASTER SOLVEWEDGEB BASED ON UNIT-UNIT DISTANCES  *********\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop (which excludes the home tractPop)\n",
    "    The wedge has a fixed starting and ending angle.  Its maximum diameter is angle-related to max diameter of the state map\n",
    "    It uses bisection, NOT scipy minimize to minimize the square error in the wedge pop vs. target\n",
    "    The method passes back the final wedge pop and list of tracts in the wedge\n",
    "    \"\"\"\n",
    "    chgLoopNo, maxLoopNo = 10., 22.  #when we will start relaxing the pop tolerance, and when we give up\n",
    "    includedAngl = ENDANGLE - STARTANGLE\n",
    "    guessedR = MAXD / math.cos(0.5*includedAngl)  #max possible wedge radius, accounting for possible wide angle\n",
    "    popTol = TOLERPOPS[0]  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    #                      We loosen toward half the MAX tractPop if not converging\n",
    "    \n",
    "    offsetPop = 88888888.  #to force a reduction the first time through the loop\n",
    "    dr = guessedR\n",
    "    loopNo = 0\n",
    "    while abs(offsetPop) > popTol and loopNo < maxLoopNo:\n",
    "        loopNo +=1\n",
    "        popTol = max(TOLERPOPS[0], TOLERPOPS[0] + (TOLERPOPS[1] - TOLERPOPS[0]) * (loopNo - chgLoopNo) / (maxLoopNo - chgLoopNo) )\n",
    "        dr = 0.5*dr\n",
    "        guessedR -= dr*np.sign(offsetPop)\n",
    "        \n",
    "        guessedPoly = buildWedge(tractCP[t],STARTANGLE, ENDANGLE, guessedR,XSCALE) \n",
    "        iCP, iClist =  getCountyWP(t,guessedPoly, tractCP, tractPop, countyTractList[hC])\n",
    "        nonCP, nonClist = getNonCWP(guessedPoly, hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyLIST)\n",
    "        offsetPop = iCP + nonCP - nontractTWP\n",
    "        #print(t,loopNo,r5(guessedR),int(iCP),int(nonCP),r3(offsetPop+nontractTWP),r3(nontractTWP),\"t,loop,R,iCP,nonCP,pop,target\")\n",
    "        if loopNo >= maxLoopNo-1:\n",
    "            print(\"WARNING. Looped\",loopNo,\"times for t,angles,R\",t,r3(STARTANGLE),r3(ENDANGLE),r5(guessedR),\n",
    "                  \"wedgePop offset, target are\",int(offsetPop), int(nontractTWP) )    \n",
    "    finalPop = iCP + nonCP\n",
    "    finalList = iClist + nonClist\n",
    "    return finalPop, finalList, guessedR, loopNo\n",
    "\n",
    "#above = bisection.  Below solveWedgeB uses static unit-unit distances\n",
    "# Also tried scipy minimize, which doesn't seem any faster\n",
    "\n",
    "def solveWedgeB(nontractTWP,T, HC, POLLY, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that intersect,\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2x2 array\n",
    "    \"\"\"\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    \n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList = 0, 0, list()\n",
    "    notFull = True\n",
    "    while notFull :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist    \n",
    "\n",
    "def solveWedgeC(nontractTWP,T, HC, POLLY, STARTANGL, ENDANGL, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST, UUANGLE):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that\n",
    "    fall within the angle range (in lieu of wedgeB's check for intersection),\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2D array\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    STARTANGL, ENDANGL = STARTANGL % (2.*pi), ENDANGL % (2.*pi)\n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList, latestDist = 0, 0, list(), 0.00001  #dummy value\n",
    "    notFull = True\n",
    "    while notFull and i < len(UUDIST) - 2 :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if isIncludedA(STARTANGL, ENDANGL, UUANGLE[TT]) : #POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist \n",
    "\n",
    "\n",
    "def getPartialTract(t, HDTRACTLIST, offsetPop, UUDIST, tractPop, neighborLIST):\n",
    "    \"\"\"\n",
    "    If offsetPop is >0, this method identifies the tract with centroid farthest from Home t that can jettison at least offsetPop ...\n",
    "    ... by slicing out part of this tract.\n",
    "    If offsetPop <0, we ID a tract CLOSEST to Home t among neighbors of in-HD tracts to pick up a partial\n",
    "    We return the tractID and its new partial use\n",
    "    We have updated this method to pass the uuDist slice for tract t instead of computing tract distances inside here\n",
    "    \"\"\"\n",
    "    debugGPT = False\n",
    "    NTRACTS = len(tractCP)\n",
    "    bigDist = 9999999.\n",
    "    qualifyingTractList = list()\n",
    "    isWholeTract = False\n",
    "    if offsetPop > 0:\n",
    "        homeDist = [0.]*NTRACTS  #default = 0 to not get picked\n",
    "        for TT in HDTRACTLIST:\n",
    "            if tractPop[TT] > offsetPop:\n",
    "                qualifyingTractList.append(TT)\n",
    "        for TT in qualifyingTractList:\n",
    "            homeDist[TT] = UUDIST[TT]\n",
    "        if len(qualifyingTractList) == 0:  #very rare case where all in-HD tracts are too small.  Jettison nearly the whole farthest one\n",
    "            isWholeTract = True\n",
    "            for TT in HDTRACTLIST:\n",
    "                if tractPop[TT] > 0.9*np.average(tractPop): #set arbitrary min qualifying pop\n",
    "                    homeDist[TT] = UUDIST[TT]\n",
    "        targetT = homeDist.index(np.max(homeDist))\n",
    "        partialUseFrac = max(0.0001,(tractPop[targetT] - offsetPop)/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(\"positive offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    else: #pick up a partial\n",
    "        homeDist = [bigDist]*NTRACTS\n",
    "        for TT in HDTRACTLIST:\n",
    "            for TTT in neighborList[TT]:\n",
    "                if TTT not in qualifyingTractList and TTT not in HDTRACTLIST and tractPop[TTT] > abs(offsetPop):\n",
    "                    qualifyingTractList.append(TTT)\n",
    "        for TTT in qualifyingTractList:\n",
    "            homeDist[TTT] = UUDIST[TTT]\n",
    "        if len(qualifyingTractList) == 0 :  #rare case where most nearby tracts are small.  Add nearly the whole biggest one\n",
    "            isWholeTract = True\n",
    "            maxPop = 0.\n",
    "            targetT = -999\n",
    "            for TT in HDTRACTLIST:\n",
    "                for TTT in neighborList[TT]:\n",
    "                    if TTT not in qualifyingTractList and TTT not in HDTRACTLIST : # we'll take just one, even if doesn't fill gap\n",
    "                        qualifyingTractList.append(TTT)\n",
    "            for TTT in qualifyingTractList:\n",
    "                if tractPop[TTT] > maxPop:\n",
    "                    targetT = TTT\n",
    "                    maxPop = tractPop[targetT]\n",
    "        else:\n",
    "            targetT = homeDist.index(np.min(homeDist))\n",
    "        partialUseFrac = min(0.9999, -1.* offsetPop/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(t,\"'s negative offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    \n",
    "    return targetT, partialUseFrac\n",
    "\n",
    "def getAdjoiners(UNITLIST, UNITNBRS): #returns all units that neighbor a UNITLIST but are not in the UNITLIST \n",
    "    allNbrs = list()\n",
    "    for U in UNITLIST:\n",
    "        allNbrs = allNbrs + UNITNBRS[U]\n",
    "    adjoiners = list( set(allNbrs).difference(set(UNITLIST)) )\n",
    "    return adjoiners\n",
    "\n",
    "def get2nbrs(ULIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    Get the combined list of first- and second-level neighbors of a LIST, using neighborList connectivity.  Excludes the source list\n",
    "    \"\"\"\n",
    "    firstList =  getAdjoiners(ULIST, UNITNBRS)\n",
    "    secondList = getAdjoiners(firstList, UNITNBRS)\n",
    "    twoLevelList = list( set(firstList + secondList).difference(set(ULIST) ) )\n",
    "    return twoLevelList\n",
    "\n",
    "def getContigFromStarter(starter, VLIST, NEIGHBORLIST):  #all contiguous units in VLIST, starting from starter unit\n",
    "    foundList, newList, prevList = [ starter ], [ starter ], [ starter ]\n",
    "    while len(newList) > 0:\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()        \n",
    "    return foundList   \n",
    "\n",
    "def isContiguous(VLIST,NEIGHBORLIST,returnBiggestPiece=False):\n",
    "    \"\"\"\n",
    "    This method determines if all items in a list form a continuous chain of neighbors based on the passed neighborlist\n",
    "    Empty lists are considered contiguous.  If discontiguous, a less-than-majority piece list is returned,\n",
    "     unless giveBig=True, in which case we return the biggest piece\n",
    "    \"\"\"    \n",
    "    isUnbroken, newList, foundList = True, list(), list()\n",
    "    if len(VLIST) > 0:\n",
    "        foundList, newList, prevList = [ VLIST[0] ], [ VLIST[0] ], [ VLIST[0] ]\n",
    "    while len(newList) > 0:  #this round's list of neighbors\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()      \n",
    "    pickedPieceList = foundList.copy()  #default contiguous sublist to pass back to main\n",
    "    \n",
    "    if len(foundList) < len(VLIST):  #not contiguous\n",
    "        isUnbroken  = False\n",
    "        pieceLists, remainingList = [foundList], list( set(VLIST).difference(set(foundList) ) )\n",
    "        if returnBiggestPiece == True:  #we were asked for the biggest piece, not a random small one, so we must find them all\n",
    "            if len(foundList) < 0.5*len(VLIST):\n",
    "                while len(remainingList) > 0:\n",
    "                    newList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                    pieceLists.append(newList)\n",
    "                    remainingList = list(set(remainingList).difference(set(newList)))\n",
    "                pieceLengths = [len(pL) for pL in pieceLists]\n",
    "                pickedPieceList = pieceLists[ pieceLengths.index(np.max(pieceLengths)) ]\n",
    "            \n",
    "        else: #quickly return a contiguous sub-list that's at most half the total units in the list\n",
    "            if len(foundList) > 0.5*len(VLIST): #pick a different piece; this one is the majority\n",
    "                pickedPieceList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                \n",
    "    return isUnbroken, pickedPieceList\n",
    "\n",
    "def enclaveCheck(UNITLIST,UNITNBRS,maxLoops=4):\n",
    "    \"\"\"\n",
    "    This method determines if a list of units has an unbroken boundary AND whether its complement has an unbroken boundary\n",
    "    If the complement boundary is broken, there is an enclave or the district is so disconnected its adjoining units aren't contiguous\n",
    "    The method returns contiguity of boundary and of its adjoiners.  Also returns the lists of boundary and complement-boundary units\n",
    "      If either of these is broken, only a contiguous sublist is returned that is guaranteed to be smaller than half (for finding enclaves)\n",
    "      maxLoops is the number of loops for expanding neighbors-of-neighbors for contiguity of the units outside the passed unit-list\n",
    "    \"\"\"\n",
    "    UNITSET = set(UNITLIST)\n",
    "    ADJLIST =  get2nbrs(UNITLIST, UNITNBRS)  #in case direct adjoiners are only queen-adjacent\n",
    "    #adjoinersOfAdjoiners = getAdjoiners(ADJLIST,  UNITNBRS)\n",
    "    #BDRYLIST = list( set(UNITLIST).intersection(set(adjoinersOfAdjoiners)) )  #this might fail for queen-adjacent boundary units\n",
    "    BDRYLIST = list (set(get2nbrs(ADJLIST,UNITNBRS)).intersection(UNITSET) )  #in case inHD boundary is only queen-adjacent\n",
    "    noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)  \n",
    "    unbroken, smallPieceList = isContiguous(BDRYLIST, UNITNBRS)\n",
    "    if not unbroken: #could be that district's boundary intersects the state boundary or there is a nonHD enclave inside the HD\n",
    "        unbroken, smallPieceList = isContiguous(UNITLIST, UNITNBRS)  #slower check for full district, not just its boundary \n",
    "    nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "    while nLoops <= maxLoops and not noEnclave:\n",
    "        nLoops +=1\n",
    "        newSet = set(ADJLIST)\n",
    "        for UU in ADJLIST:\n",
    "            newSet = newSet.union( set(UNITNBRS[UU]).difference(UNITSET) )\n",
    "        ADJLIST = list(newSet)\n",
    "        noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)\n",
    "    #isJiggy = noEnclave and unbroken\n",
    "    return unbroken, noEnclave, smallPieceList, enclaveList\n",
    "\n",
    "def getBdryNonEdgers(UNITLIST, UNITNBRS): #all in-district boundary units that neighbor a non-district unit\n",
    "    ALLnonHDnbrs = set()\n",
    "    for UUU in UNITLIST:\n",
    "        ALLnonHDnbrs = ALLnonHDnbrs.union(set(UNITNBRS[UUU])).difference(set(UNITLIST))\n",
    "    BdryNonEdgerSet = set()\n",
    "    for UUU in UNITLIST:\n",
    "        if len(set(UNITNBRS[UUU]).intersection(ALLnonHDnbrs)) > 0:\n",
    "            BdryNonEdgerSet.add(UUU)\n",
    "    return list(BdryNonEdgerSet)\n",
    "\n",
    "def getEnclaveLists(UNITLIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    finds ALL units enclaved by a UNITLIST, parsed into lists of contiguous pieces\n",
    "    We assume the largest contiguous complement to the UNITLIST is not an enclave, but rather the majority of the HD complement\n",
    "    if the UNITLIST's complement (all couldBeEnclaved) is contiguous, the returned list will be blank\n",
    "    \"\"\"\n",
    "    eSets, nU = list(), len(UNITNBRS)\n",
    "    offmapList = list()\n",
    "    for i,L in enumerate(UNITNBRS):\n",
    "        if len(L) == 0:\n",
    "            offmapList.append(i)  #offmap list are units with no neighbors (were surrounded)\n",
    "    complementSet = set([i for i in range(nU)] ).difference( set(UNITLIST + offmapList) )    \n",
    "    remaining2nbrs = get2nbrs(UNITLIST, UNITNBRS)\n",
    "    \n",
    "    isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS) #quicker than contig check on entire complement\n",
    "    while not isContig:  #this will kick out before writing the final sublist = map majority\n",
    "        starter = shortList[0]\n",
    "        newEnclaveList = getContigFromStarter(starter, list(complementSet), UNITNBRS)\n",
    "        eSets.append(set(newEnclaveList))\n",
    "        remaining2nbrs = list(set(remaining2nbrs).difference(set(shortList)) )\n",
    "        isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS)\n",
    "        \n",
    "        # couldBeEnclaved = list( set(couldBeEnclaved).difference(set(newEnclaveList)) )  #revised 18-Feb-24 -- was slower\n",
    "    fused_eSets = set()\n",
    "    for i, eSet in enumerate(eSets):\n",
    "        fused_eSets = fused_eSets.union(eSet)\n",
    "        for j in range(i+1, len(eSets) ) :\n",
    "            eSets[j] = eSets[j].difference(eSet)  #in case contiguity occurs, but not in 2-neighbor list; avoid double-count\n",
    "    remnant_eSet = complementSet.difference(fused_eSets) \n",
    "    eLengths = [len(eSet) for eSet in eSets]\n",
    "    if len(eSets) > 0:\n",
    "        if len(remnant_eSet) < np.max(eLengths):  #exchange the small remnant for the biggest found \"enclave\" (usually the major complement) \n",
    "            eSets[eLengths.index(np.max(eLengths))] = remnant_eSet.copy()\n",
    "    eLists = [list(eSet) for eSet in eSets]\n",
    "    return eLists\n",
    "\n",
    "def wontEnclave(proposedU, dList, NBRLIST, mapBDRYLIST):\n",
    "    \"\"\"\n",
    "    This method checks if adding a proposedU to a dList (list of units in a district) will create an \"enclave\" of units in the dList's complement\n",
    "    via two problems:  A) the dList will now have a discontiguous set of units on the map boundary.  The full map boundary list is mapBDRYLIST\n",
    "      B) The non-dList neighbors of dList units aren't contiguous\n",
    "    The method doesn't require that the dList wontEnclave without the proposedU included; it just checks the proposedU + dList combination\n",
    "    It also doesn't check that the dList is itself contiguous with or without proposedU\n",
    "    In that sense, it is more limited than enclaveCheck\n",
    "    \"\"\"\n",
    "    wontEnclave = True\n",
    "    newList, newSet = dList + [proposedU], set(dList + [proposedU])\n",
    "    unitsOnBoundary = list( set(mapBDRYLIST).intersection(newSet) )\n",
    "    wontEnclave, __ = isContiguous(unitsOnBoundary,NBRLIST)  #first check - does district touch MAP boundary in multiple places? (quick FAIL)\n",
    "    if wontEnclave: #slower check below for internal enclaves\n",
    "        adjoiners = get2nbrs(newList, NBRLIST)  #2-level to protect for queen adjacency in boundary        \n",
    "        wontEnclave, __ = isContiguous(adjoiners,NBRLIST)\n",
    "        if not wontEnclave:\n",
    "            nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "            maxLoops, ADJLIST = 6, adjoiners #unlike enclaveCheck, here we just arbitrarily set a number of loops for search expansion\n",
    "            while nLoops <= maxLoops and not wontEnclave:\n",
    "                nLoops +=1\n",
    "                adjSet = set(ADJLIST)  #align nomenclature w enclaveCheck\n",
    "                for UU in ADJLIST:\n",
    "                    adjSet = adjSet.union( set(NBRLIST[UU]).difference(set(newList)) )\n",
    "                ADJLIST = list(adjSet)\n",
    "                wontEnclave, __ =   isContiguous(ADJLIST, NBRLIST)\n",
    "        \n",
    "    return wontEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d041d328-83e0-45db-a869-e0956c23ee26",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "Interrupted by user",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m STATE \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28minput\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEnter the state postal code; e.g. OH \u001b[39m\u001b[38;5;124m\"\u001b[39m))\u001b[38;5;241m.\u001b[39mupper()\n\u001b[0;32m      2\u001b[0m popFilename \u001b[38;5;241m=\u001b[39m STATE\u001b[38;5;241m.\u001b[39mlower()\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_pl2020_vtd.dbf\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOK, enter 1 below to use\u001b[39m\u001b[38;5;124m\"\u001b[39m,popFilename,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas this state\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms population data file\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py:1202\u001b[0m, in \u001b[0;36mKernel.raw_input\u001b[1;34m(self, prompt)\u001b[0m\n\u001b[0;32m   1200\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraw_input was called, but this frontend does not support input requests.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1201\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m StdinNotImplementedError(msg)\n\u001b[1;32m-> 1202\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_input_request(\n\u001b[0;32m   1203\u001b[0m     \u001b[38;5;28mstr\u001b[39m(prompt),\n\u001b[0;32m   1204\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_parent_ident[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshell\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m   1205\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_parent(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshell\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m   1206\u001b[0m     password\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m   1207\u001b[0m )\n",
      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\ipykernel\\kernelbase.py:1245\u001b[0m, in \u001b[0;36mKernel._input_request\u001b[1;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[0;32m   1242\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[0;32m   1243\u001b[0m     \u001b[38;5;66;03m# re-raise KeyboardInterrupt, to truncate traceback\u001b[39;00m\n\u001b[0;32m   1244\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInterrupted by user\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1245\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1246\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m   1247\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog\u001b[38;5;241m.\u001b[39mwarning(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid Message:\u001b[39m\u001b[38;5;124m\"\u001b[39m, exc_info\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
     ]
    }
   ],
   "source": [
    "STATE = str(input(\"Enter the state postal code; e.g. OH \")).upper()\n",
    "popFilename = STATE.lower()+\"_pl2020_vtd.dbf\"\n",
    "print(\"OK, enter 1 below to use\",popFilename,\"as this state's population data file\")\n",
    "enter1 = input(\" \")\n",
    "if int(enter1) != 1:\n",
    "    popFilename = input(\"OK, then enter the pop data file.  Typically a xx_pl2020_vtd.dbf\")\n",
    "print(\"attempting to read in state unit geometries.  Stand by ...\")\n",
    "tractPopFile = gpd.read_file(\"state_map_files/\"+popFilename)\n",
    "trueTractGeom = tractPopFile['geometry']\n",
    "nTracts = len(trueTractGeom)\n",
    "tractPop = tractPopFile['P0010001']\n",
    "statePop = np.sum(tractPop)\n",
    "censusGEOID20 = tractPopFile['GEOID20']\n",
    "print(\"I read in the Census popn data.\",STATE,\"has\",statePop,\"peeps and is divided into\",nTracts,\"shapes.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3bfdea9d-b0ea-497a-a556-6b488a3e333e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the number of districts in the state.  My guess is 9 9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 14 counties in MA .  I will assign them county Nos 0 - 13\n"
     ]
    }
   ],
   "source": [
    "tractGeom = trueTractGeom.copy()   #for some states, we will modify geom, so preserve original\n",
    "#tractNAME20 = tractPopFile['NAME20']\n",
    "tractPop = tractPopFile['P0010001']\n",
    "inputfileCountyNo = tractPopFile['COUNTYFP20']\n",
    "vtdGEOID20 =  tractPopFile['GEOID20'] \n",
    "nDistricts = int(round(statePop/760000,0))\n",
    "nCutDistricts = 0\n",
    "nDistricts = int(input(\"Enter the number of districts in the state.  My guess is \"+str(nDistricts)))\n",
    "tractArea = [tractGeom[t].area for t in range(nTracts)]\n",
    "trueTractCP =   [tractGeom[t].centroid for t in range(nTracts)]\n",
    "tractCP = trueTractCP.copy()  #for some states, we move secluded corners into the map, so save the true data\n",
    "tractCPx =  [tractCP[t].x for t in range(nTracts)]\n",
    "tractCPy =  [tractCP[t].y for t in range(nTracts)]\n",
    "inputCountyNumbers = list()\n",
    "for t in range(nTracts):\n",
    "    if inputfileCountyNo[t] not in inputCountyNumbers:\n",
    "        inputCountyNumbers.append(inputfileCountyNo[t])\n",
    "nCounties = len(inputCountyNumbers)\n",
    "print(\"There appear to be\",nCounties,\"counties in\",STATE,\".  I will assign them county Nos 0 -\",int(nCounties-1))\n",
    "sortedICNs = list(np.sort(inputCountyNumbers))\n",
    "countyNo = [sortedICNs.index(inputfileCountyNo[t]) for t in range(nTracts) ]\n",
    "\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "adffc7a4-a78c-477f-8ea4-7f8e5109fa2a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now build the county-by-county geometries, hoping there are no islands\n",
      "Building county geom for county 0\n",
      "Here is your original county-base map before any triage; e.g eliminating coastal unpopulated tracts w pop < 4.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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ly5e3JrzOK2sLRA8FdZvmpc3ikGVUnejLjSRJznE+Wi0eXt5tem67zepMdurrncmQyYSlvh6LqQ5LfT3mutqTlzosdbWYa2sx19dRVVqMua6O+uoqTNVVHN+9A6Of/8mkyYwsN92ypFKrTyc9p8YrnZUE/T4hOnXfo2Fc05mJ0qnbaq1WfMNtJ/kF32AyZdOv35sUZT6PTT6hdEguIbZfEDf8fSh6Tw3eAS1vHZYkieBob4KjvRl1XQLbv8tgx8oMIpP8iUj0a/uABUV0/CeicPEcDufg3RH3K/L0SrTUuCu1RouHV+uTJZvNwn9m/4HE4aOZNu9R4HTLku2MLrjT3W7ms1qRTnfFNb5fX13V6BjbycfttqbX2ZEk1e+SHj2aM1uKTiZSDirxjzcT2j0cldoDtcpw8toDtdqA6tR1o8cMqNUeSFLXG8NksZSRnv4SoaHT8PbqiYc+jjrHPqXDchmnZntdLJVaxbBp8eSlVbD6vUPc+PgwDEbRstkZiKTGHZUcA1MFxAxX5OmdSc25W9XsVgs1+ZlU5x7HOzQC3/h+59xPaB6NRodao6U8P7dh25ktS23dFQfgsNvPTpROdqlZz0qkzrx/+nZNeQ1Wk5myvHR0h6vpfX0Ndns9DocJaN6K0pKkQ602oFZ5oFI7E51TSVDDNpWHMxFSG1CpDKeTpbMSp9P7O89z6nx6JMl1Bo2mpb+ALNtJTHwMAG+fRMz1VVRVFOHjF6JwdJ2LSiVx+R19WP6P7WxYnsKkO/soHZLQBkRS446ytoCkhqihijy9DBRUwLcPXo2HQYvZrqa6xkx1nY066+lv1756K3d9uEqRGDsTg5cXNWWlHfZ8KrXaObbH09j0zk1Y+db/kfbbcfomvUVAqLOL2eGw4HDUNyQ5dns9dkc9DvvvbjvqcdjrsTfcPvPahM1aidlRgN1uajjfqdsOh7np4E693oYEx9Co9aghYTrVgnSubarTCZUzaTrjuuG2/mTydOEuu/LyreTnf0XPHs+i1wUBEBjSg5ITUJRzRCQ17cA7wMDIa+PZsDyFUX/ojpd/J50t24WIpMYdZW2DsGTQXfyHTmsMvHo6Hqu/obbWTFm1Fb3KTIifnoT4ELyCQvEOCefrT1YRFSLeINqCd1AwhcfTlA6jVUZe838c3zaPL5+dx62LPsRgDECt1qNW69Fq/drteWXZcTphOjPpcZhwNCRT50qcTu9jP3m8xVJyxrZT53MeL8stKd2vOp0AqXRnJFEGVCo9lZV78PUdTETEjIYjQiJ7cTRTorz0GHBpm/+cBEgcFsamr9JI2V7AoCu66MSTTkQkNe4oeyskTW56v3YSM+4GYsbdcM7HTFVVSOZyPD9fid5LfLNsCwGR0RSkpWCqq8Pg6al0OC0SGNaTqX+Zy3+fW8yXL97L7Cc+Q9UBg9slSYVa7Yla3b4/L4fD5kyYHOazWpIcJ7c5HGZnEuQwN2xreNxhwmF3bjeEXEVc3IONusN0Bk/spiBqxQyodqP30BA/IJijW/IZOClGDIB3cyKpcTfVBVCeqdh4mgvZ9OztbNtfDIBaUhM3coLCEXUOYfHdObz+F3IPHyBhiOv93psS12cSl96RwdqlK/jvq/cw9f7X0OrcKzk7H5VKg0rljYa2nTV3Jskeg0XOaLfzC9BzRBjf7Sik6EQ1obHNq4AvuCbXGSEnNE/WVud1dMcX3buQytSdbNtfTLy/iYmXJnLH8/8k9orblA6rU4jq5Sx4mZtyROFIWm/QhHvpP7UfGVuK+PCxWUqH41b0mm7I6hylw2i1RYsWIUkSc+fOVTqU84rqFYDRV8exLflKhyJcJJHUuJvsbeAXAz6uU0V0278e4J3HnwIgKDqG/vf/G5/Y3soG1YkERjv7+Y9t3shPbyzGYjIpHFHrjL/pWSIHelKRZWf/xmVKh+M2jF4JqD2KMJvqlQ6lxXbs2MHSpUvp18+1Z0GqVBLxg0I4cbhM6VCEiySSGneTtRViRiodRYOq9D1s2u4sDnb3439mzF9eUTiizkelUqH3NFJVXMihdWvY8Ml7ZB060PSBLkalUjH9Lx/hGy2x9ZuvlA7HbfgH9USSZIqyjyodSovU1NQwe/Zs3n77bfz9O3Z9utYIivKiuqQem8Xe9M6CyxJJjTux1EL+Poh2nXEV3jF9CPexosJBeV4WNXkpfPPgNFY/NhO72f2+Wbqqu159hzkvvgbAvp9/4MtnHqU4K1PZoFpBo9UTlhiDqUJ8cDSXSnIOfTRbShSOpGUeeOABpkyZwsSJE5UOpVkCwo3IMlQU1SkdinARRFLjTnJ3gWxXZBHL85G0OqYu+A8R/hJfvfc1Sx99gowiB/vTaijc/qPS4XUaBi9vgrvFMnL6LBKGOH//Hz3yJ9558C6FI2s5SSUDYoZJc6TsX01K2pPYzd50S7pE6XCabfny5ezevfusRYtdmX+Yc/B6WX6twpEIF0MkNe4kaxvofSG4l9KRNOId05Ppr/6X5N7h9E/yZcp1znoaOm/Xb3J2N6NumM1VDz5MYFQMGp2eyqICpUNqMZvFgkpUpG9SaX422SX3oTFmEur3N7Ra96j7lJ2dzUMPPcTHH3+MwdDyNZqUovfU4umrozxftNS4M5HUuJPsrRA9DFSu92tTa7VMevJtJv7jEzhZnyb7kFizpj3oDAZu+9cSeo+9DICaCvca3Giz2FBrREtNUzy8vbFUxwJgsVThcDS9iKkr2LVrF0VFRQwePBiNRoNGo2H9+vW88soraDQa7HbX7XoMCDdSLlpq3JrrfToK5+awQ/Z2l6xP83tx464DoODIHoUj6dwCo2MAKExLVTiSlrFZrKi1Tb/1mOoqyM/c2QERuSZPLz8mXPkdUu1kquwvsXblvZjqa5QOq0kTJkzgwIED7N27t+EyZMgQZs+ezd69e1GrXXehUv9wo+h+cnOi+J67KDoC5iqXq09zLnovH0J9HKjsYqBwewqJjQegKPO4WxXls1tsqLUqbFYzkkpCrdadc79N377IvhX78e+mYdi1N9B7xCxULthK2Z50ek/GT32N3Zvep9TwIuvXTKV//yWExbhWF/SZvL296du3b6NtRqORwMDAs7a7moBwI4fW52K3OVBrutbfWmchkhp3kb0VVBqIHKx0JM1it8uou9gHUEfbu+p7AMx17vXN0moxU11k4tXbrsNhU4Eko1LLqDTOdVpVGlBpJGwmB5JKhbXexqr/fMZvyz9j0NVXMmjCPedNhDqrQWNuJ+f4AA4d/j8OHJlOUf6T9Bs+o+kDhRYJCPfE4ZCpLKonIEKZtfWEiyOSGneRtRXC+4MblJe3W0yU1Ur0P9k9IrSP0PjuHP1tPSHx3ZUOpUV0eg9k2UzC6FgCwqOxWc3YrBbsFgs2qwWb1YrdZsVuteEfFs74m57l0JZP2P6/r9nw7k9s/+YH+k0aw/ApD6HTd50Pnqj4gQQEf89vax+kuPZR1n2/g9GTFrrFAOJ169YpHUKzePk7BzbXVplFUuOmRFLjLrK2Qa+pSkfRLGVHtuFARXCSa1cRBXjqqad4+umnG20LDQ2loMD1ZxUVnzgOgKXOvWZr/OEvbyLLMgZPv2Yfkzz6FpJH30Lq3pVs/WYZ2z/fzJ6Vm+h9WT9GXfswnt5B7RewC/H09mPC1cvYvnYxNfo3+fXHwwwb8Sb+IdFKhyYILkEkNe6gKg8qs9xikDBA8eHtAAT1HaNwJM3Tp08f1qxZ03DflQcynunwhrUA9Bo7TtlAWkjv4dvqYxMHXE3igKvJSd3Epq/eZN8PBzi4eg7dRycw9vq/4BsU23aBuiiVSsWICfNIPzSItBN/YceuacRGvkhSv8uVDs3t1VSYAdAZxEejuxK/OXfgootYnk9JZio+Ogv6kFilQ2kWjUZDWFiY0mE0m8VUz76ffwBJAllGdrjuFNn2EpU4hpmPjqE49yAbv/oPqRvSSdlwP92GhHPJjLkERyYrHWK7S+gzjqCwlWzdfAvZJfdh3fkvzOZK9B4e9BowvcsNqm4Lx/cU4+mrIySm/VZdF9qXSGrcQdZW8I8D71ClI2mW4vwigvzcZyBnamoqERER6PV6hg8fznPPPUd8fLzSYZ3TqjcWc3Dd6VYllVqNzqPr9v0HR/blDw+9TVVZFhu//hepG1P5cPt8hk0fzdjr/650eO3ONzAcjTEDgJqKUmpUz4EZTL+VMnjsHy94rMPhICd9F0ERiXga/TogWtcmyzLpu4tIGBCMpBJ1lNyVSGrcQfZWl1oaoSkl5Wb69HSPlo/hw4fz4YcfkpSURGFhIQsXLmTUqFEcOnSIwMBApcM7y+GNa0GSmHTP/xGTPBDf4BClQ3IJPgExTLn7P9TfVMbbf5pNSU6W0iF1CLOpquG2SuMAB9iqkynVLsNmuxON5vSXC1mWqaksprQwjZLCA5RWfIHGK5Os43cw5orHlAjfpRRmVFFTbiZhkPifcmciqXF15mooOABD7lA6kmapLyukxqImqJt7zMiZPHlyw+3k5GRGjhxJQkICy5YtY968eQpG1lhtRTkfzX8Ih92OT3AIyeOvUDokl6QzeGM3S3gHBSsdSofQG3zw5A7qeI8qx/NYq5JJSppPRsFsVn58D/6hUVgs2TikPCRdAWrd6UHlktwfh9VAnfw5JQU3ERQWp+ArUd7eNVn4BBkIT/RTOhThIohOV1eXsxNkh/uMpzmwCYDgXoMUjqR1jEYjycnJpKa6VpXe0pxsasvLUKnV3L74TaXDcVml+cdwWFXUldiUDqXDjBz/GH5aZ1dbUMAU4nuPoDZzNsbILdTbf8JBORqpO56qWQR5LKR71KcMH7yDSdd8w4hha5HtBnZt/yNWi0nhV6Kc0twa0ncXM3hyLCrR9eTWREuNq8veBgY/CEpSOpJmKUnZh1py4NdrtNKhtIrZbObIkSOMHTtW6VAaCU/sAUDSiDGNuhSExsoL0wGI7Nlb4Ug61uCxd1JScBkBwbEAXHXzk9RUPIJfyIXHW3n7h5AY/zLHc+/gt9WPM27KPzsgWtciyzJbvk3HO8BAj+Hu0W0unJ9oqXF1WVsherhLLmJ5LsVZGQR42lB7+igdSrM8/PDDrF+/noyMDLZt28b06dOpqqri1ltvVTq0RrR6PSq1mvL8XKVDcWl11YUAdEt2j/IHbSkoLB6V2vk+odGpm0xoTonvNQYv1f3YPb7l4I5v2zNEl5S2q4gTB0sZe2OiWBqhExC/QVdmt0HODvcaJFxUTnCA+8zGycnJYdasWfTo0YM//OEP6HQ6tm7dSrdu3ZQOrZENn36ADNSUlSodikurKnEmfX4nWyyE5hk27k/YanqQm7tc6VA6lKnWysbPU0gYGExc/64xDquzE91PrqzoEFhq3CapkR0OSqocJPaMUDqUZlu+3PXfxDd/+Qk7/vcVAHWVFcoG4+JqyorRespotKKLrjlyMw5SV12ILFvx0PXHJK2gvqYODy/XX46lLWz5Nh271cHYG92je19omkhqXFnWNlBpIWKg0pE0S+WJI1gdKoLjXXcFYXdUVVwEgFdAIFG9+igcjWurKa9A7+0eFaGV5nDYOZx6AyqNxblBB2qgrrq0SyQ1x/cWc3hTHpfOSsLo5/rrZwnNI5IaV5a9FSIGgNZD6UiapfjgZgCC+rpHy5K78A0NB2DKn/4mkpom1FXW4uHrHv8vSju2fwUqjYXq3IEEBPXGZN9Mt+iHCQzv3OtIWS12tnydxoH1ucT2C6LP2EilQxLakEhqXFnWNuh7ndJRNFtJ+mEMaivG2AFKh9KpRPZwtnzlpRwRSU0TTFUWQuLE2IimyLJM1ol3keUkrrn5SySpa0xjLs2t4aelB6kpM3HJzCT6XhrZZV57VyEGCruqimyoynGb+jQAB4tMVET25uO33mD72l+oq61ROqROIay7s7+/KPO4wpG4NlmWsVQ7u+nOxVxf3cERua6UvavReB8hOuquLvOhnrqzkK9e2Ilao2LGY0NJHhfVZV57VyJaalxV9jbndbT7TE2t1XuBRsfxgiLSi0r5Yd16PJAJCQige48e9Bs+Al//AKXDdDs6gweSSkV1SZHSobg0U30xNpMa78DGtUbsdivfvf5/pP+Ww6BrR3LZrMcBOLj5Q/at+YGw7vH0HDGNiLjhXeZDrqRoF3bZSK9B7tMSfDGyj5bx8zuHSBwaymW39ESrE+OuOiuR1LiqrK0QkABe7tGUbrfZqJINXNnbhyE3zOPY/r0c3reXnNxcsktKOVG+nV+2bEMn2wn29SW+e3f6DRtOcLj7zJRSkkanp7aiQukwXFpZobMKtG9QVKPtG75cSPpvuXiFqtn9362c2H89xgAjWbtLMfjIFB7Zx97/7cfg5yC8Zxg9R19B72E3KvESOozZUoIs+XaJlbwt9TZ+/fAIkT38uPz23mKxyk5OJDWuKmsrxIxUOopmqyk8jgM1O1ILidj3K70GXU6vgYMB52rAmSlHObR7FyeysiioqCR3zz427tlHkIeO/3uk86+mfLEMRiOmWtF9ciEVRc7Vqv1CTq9hVFtVyIFV24lI9mfmY5+w4ZunSd2ym+zdpcQPj2Pa//0bi7mao9u/Jm3nb+QeLCBj60d4PBFIXJ+JSr2UdmezlSFp/JQOo0Ns+ioVc62N8fN6iYSmCxBJjSsyVTlr1Ay/R+lIms0nLIHrBwXz9W7IOrqXqEGXNzymUqmI79mb+DNK1+edyOTLj5ZRXluvRLhux9PXj9qKcqXDcGmnCu8FhJ1eTDXz0C9Y69QMv+Y2JEni0uuf4tLrneNvTnU1eWgCGHjZ3Qy87G4s5hpeu30Gx/et79RJjV0uR6fyVzqMdpd5oIQjv+UzbnYPfILErLiuoPO3PbqjnB1utYglgKRWEz1wAgBBYVFN7A0R3WIJCgzE0d6BdRLBsfE47HZ2fveN0qG4rOpS55ij9V88w5EdX2Gur+bEwR1IagfhcUMa7Xu+sTM6vRfeYSoKUtNbFYPNZuPxxx8nLi4ODw8P4uPjeeaZZ3A4XOwvXVWJJBmUjqJdORwy6z45RkyfAHqPEd3cXYVoqXFF2dvAIwCCEpWOpEVKMw8BENitZ7P2N3p5IatKWPX1F1xx/Yz2DM3tTbjzPtK2b2b9J+8TnTyA0Nh4pUNyOUlDriD3yHGOrD3KoVUpSKr3kR0SoEKSmj8wNCgumJy9RY1ac5rrhRde4M0332TZsmX06dOHnTt3cvvtt+Pr68tDDz3UwlfUfgyaYTgMX7H6577Ya3sgSxXoVUMZP+15pUNrM+X5tdRWmJ3jaLrIAHBBtNS4pqwtzqUR3OwfsSQ/CxV2/GKaV0tl9MRJAGRmZLZjVJ2DRqPjxqdeQAK+eHo+NotF6ZBcTmzvSdy+6Fv+9MF/ue6JB+k/dQD+sSq8QsFuMzX7POHde2CpkRpW/G6JLVu2cM011zBlyhRiY2OZPn06kyZNYufOnS0+V3saM+kfSHWTcdg0qHU16HwysUiblA6rTRUcr0RSSYTEusfiukLbuKikZtGiRUiSxNy5cwGwWq088sgjJCcnYzQaiYiIYM6cOeTl5V3wPB988AGSJJ11MZkavxEtWbKEuLg4DAYDgwcPZuPGjRcTvmuy2yBnl1tN5T6ltKSEAHUd6mauu+Pt5w+yTGhoSDtH1jkERXdj3K13Y6mr48t/PKZ0OC5LrdYR3+cKJtz0LHe8sIJ7X1mJ0Te02ccX5+0HwGxu+eKhY8aM4ZdffiElJQWAffv2sWnTJq666qoWn6s9aXU6xl/9GldctZfLp6xCMo1BrS93vW6yi1BwvJLASCNavZi+3ZW0OqnZsWMHS5cupV+/fg3b6urq2L17NwsWLGD37t188803pKSkMG3atCbP5+PjQ35+fqOLwXC6z/fzzz9n7ty5PPbYY+zZs4exY8cyefJksrKyWvsSXFPhAbDWus0ilmcqqTYR5NH81qWMo4dBkoiKjWt6ZwGAxGGjkFQqUYivncgOB5lbS/CNrSYsZliLj3/kkUeYNWsWPXv2RKvVMnDgQObOncusWbPaIdq2Exp6BSqNiaz0ztNaU3C8irB4X6XDEDpYq5KampoaZs+ezdtvv42//+kR9L6+vqxevZoZM2bQo0cPRowYwauvvsquXbuaTD4kSSIsLKzR5Uwvv/wyd955J3fddRe9evVi8eLFREdH88Ybb7TmJbiurK2g1rvNIpZnKjWpCPQ1Nnv/kpMLNYZFiLVXmsNmsfDRI39Cdji4eu7flA6nUzqy7VssVTpGXt+6Sruff/45H3/8MZ9++im7d+9m2bJl/POf/2TZsmXtEG3b8fKOBaCqrHUDpF2NqdZKRWEdYXGi66mraVVS88ADDzBlyhQmTmx6ymNlZSWSJOHn53fB/WpqaujWrRtRUVFcffXV7Nmzp+Exi8XCrl27mDRpUqNjJk2axObNm897TrPZTFVVVaOLy8va6kxoNO61aqylropK2UhQcPOb+f1OVheuKC1pr7A6DVmW+eSxP1NfXcXYm24jYbD7dU+6g90/fYVnoJ1eQ1rXsvLXv/6V+fPnM3PmTJKTk7nlllv485//zKJFi9o40rYjyzJHU+cCEBl3qbLBtJHCDOd7fahoqelyWpzULF++nN27dzfrn9RkMjF//nxuuukmfHzOnzH37NmTDz74gBUrVvDZZ59hMBgYPXo0qanOCqElJSXY7XZCQxt/YIaGhlJQUHDe8y5atAhfX9+GS3S0i68+K8vOmU8x7veBVXbcOQ4hMLL5s3KCTlYTLisW5f+b8t3Lz1GSdYJeY8Yx7JrpSofTKZUVpFF4rIoeYwegUrVuYmhdXd1ZVXrVarVLj1WRJAnZYcBe04/AkM4xq64wswq9UYNvsKhN09W06D83Ozubhx56iJ9//rnReJdzsVqtzJw5E4fDwZIlSy6474gRIxgx4vQYktGjRzNo0CBeffVVXnnllYbtv28ObmrK5aOPPsq8efMa7ldVVbl2YlORBdX5blWf5pSSHGcCGhSf3OxjgkOdXYzl5aKo3IX89vlHpG7fQmh8d6568GGlw+m0tq18A5VaZthVD7b6HFOnTuXZZ58lJiaGPn36sGfPHl5++WXuuOOONoy0bZUWZqDSlWJUTVA6lDZTfKKK0G4+Yip3F9SipGbXrl0UFRUxePDghm12u50NGzbw2muvYTabUavVWK1WZsyYQUZGBr/++usFW2nORaVSMXTo0IaWmqCgINRq9VmtMkVFRWe13pxJr9ej17tRN07WVue1O858KszDAxOegc0fH6PRalHZbWRkZpJx9DBxZ1QcFpyObFrP1m8+x+jnz6x/vKR0OJ2W3WYhbcsRIvsF4OXb+kJtr776KgsWLOD++++nqKiIiIgI7r33Xp544ok2jLbtVJYVsWvPNGSHgT5D7lI6nDYhyzKFJ6rpPTpc6VAEBbQoqZkwYQIHDhxotO3222+nZ8+ePPLII40SmtTUVNauXUtgYGCLg5Jlmb1795Kc7PzWr9PpGDx4MKtXr+a6606vKrt69WquueaaFp/fZWVvhaAkMLb8Z6a00vJKgnS2Fh8XFRRAVmkFy5Z/gdZuJSokmLjERAYMH4WPf+cv434hNRVl/PjaP9Ho9cx56VXUGq3SIXVa+9a/j6VGxZCrZl/Ueby9vVm8eDGLFy9um8DaWUleKmpdHbGhb+HXgi8krqy2wkx9lYWQbmKQcFfUoqTG29ubvn37NtpmNBoJDAykb9++2Gw2pk+fzu7du1m5ciV2u72hdSUgIACdzlm/ZM6cOURGRjaMy3n66acZMWIEiYmJVFVV8corr7B3715ef/31hueZN28et9xyC0OGDGHkyJEsXbqUrKws7rvvvov6AbiUrG1u2UoDUFJrI8S75R+6dzw0j5rqKrat/ZUjhw6RWVRKRmkFv27dgScOosLC6D90KL0GDEKl7lr1JsrzcpFlmaFT/4Cnj5/S4XRq+39ZhWegTHzyZKVD6VAWcw0AGm3n6aYpynQu/CqSmq6pTZdJyMnJYcWKFQAMGDCg0WNr165l3LhxAGRlZTUaTFdRUcE999xDQUEBvr6+DBw4kA0bNjBs2Ok6ETfeeCOlpaU888wz5Ofn07dvX3744Qe6devWli9BOfUVUHQYRj6gdCQtJjsc5Fl96B3QurVkvLx9mDDtWiZMuxarxcK2X9eQm5NNbn4+KXn5pHz3Par//o8ATw+6JyYyZtIVePl0/lkNYQnOZTIqCs8/GF5oG95BfpSmF/HN4tuYev/raHXNL03gzrr3vYSsNUEcO/wq3ZJGKh1Omyg6UYWnjw6jX/OKgAqdiyTLsqx0EB2lqqoKX19fKisrWzzOp92lroZPpsODuyEwQeloWuTYr5/x2YZjzLokiR7jb2rTc5vq69m35TcO7d9HQWk5FrUzDx/epyeTb5jZps/lil6eNY2w7knc9I9/Kh1Kp+ZwOFj/+UJ2/28b/aYM4PJbnlU6pA6zZ/MHlJn+QbfgD+iePFbpcC7ailf2otaomHJ/v6Z3FtxGcz+/xdpPriJrK3gGQYD7Taksys0AIGlc2ycZBg8Pho+fyB1z/8Lf/7GQiSOHgyyz7dBRfvj8szZ/Plej1RuoLStTOoxOT6VSMeb6h1CpZTTqzr169e/1H3Eztrpo0tJexGF33annzSHLMkUnqgjp5q10KIJCRFLjKrK3ueUilgDFpRVE6aqQVO3/5zTmisno7FYAykobr81jMZs4uHMHKz/7hBOpKe0eS0fw8PahvqZa6TC6hPzM7ThsKsLiu9Y3fJVKQ2zMPLS+hzmwbYXS4VyUqhIT5lqbGE/ThbXpmBqhlexWyNkJ491zkcKiGhsRvh3Xf60+OWA4K7+A/328jLy8fCpqajCjgpOJVX5eHnf/5a8dFlN78QoMpKq4UOkwuoS8NGcV88gk96sTdbGSkqeS9f1SskreZPuf/Um+NIoR17pXNzg4x9MAoqWmCxMtNa4gfz/Y6t2y6J7DZqXE5klwUMdNQ79v7p9R2W1Y1Br2pB6nrKYGH09PkuO7MW3iBFR2G2pN55gp5RcagSzL1Ne4wRIfbq7oRBpaTxmfgOYv9dFZSJJEz96PUpE+Fku9nV0/naC2wtzwuMPm4KsXdvLB/N94768bWfHKXuWCvYCiE9V4Bxjw8BaDhLsq0VLjCrK3gsYA4f2VjqTFKrIPY0NDcGTHrbTt6x/AzTfNorK8nB79B+Bp9Gr0+MpVq9Dr3P9NzeFwkLl3JwBVxcV4eIkm9fZUnluMd2jXLasfHT8aD+NOTqXPX72wk1sXjQZg9+osCjOqUKklHHaZvNQKxeK8kGIxnqbLE0mNK8jaAhGDQON+H8TFGYcACG7B8ghtIb5Xn/M+5pAkdLr2qSRdX1dLyv59ZKSkkF9QQM/evbns6mnt8lyF6anUVjiXkAiJdb8B5O6mutBEdL+OS85dUbdecRQedt6uKTfz6dPbqCk3YTPbUWtU3PPKJSx7dDMOm+tNmpUdMkVZ1Qy+spOU+RBaRSQ1SpNlZ9G9gTcrHUmrFOdlocOCT0Si0qEA4LDbQVKhM1xcUiPLMmUFeaQeOkhWZiZFxcVU1tVjVWmcg7llGSSJ6p072y2p2fjZMgAGX32dWMOmndVU5mCuUhMS213pUBSVvqccz6A64noN4PCmPMrzaxseG3NjIiqVCnOtDZ8g15shVlFUh9VkJyRWtGh2ZSKpUVp5BtQWOWc+uaHi0jKCdbYOmfnUHPW1tSBJTS64+nuVpSVsXvMzh9OPU22xobaYsGv1JxMYB3pkAry9CA+PID6pB0nJ/fjsnbfIKS5t+uS/YzGbAAndBdYl+/WDt8g+tB9PXz8umX17i59DaJncNOe6axHdBykciXJqq4spOxFKr0vrGDejJ6OndydjfwnRvQKoKqknNNaXktwa7DYHoXGulzgUnThZSThGdD91ZSKpUVrWNud11FBl42il4hoboa1YHqG91FY7RwQYPJoeG1GSn8e2tb9wLCWFKgegOj24ODwsjKjoaLr36ElsUg80mrP/VYKDQzhRVkldTTWeXud+IzXV1ZGyfx/HU46RX5BPRU0tZpUGg93K/H+cu8Cb1WJmz4/fAXDPkvcbVd8W2kde+n6QZMLjhzW9cyd1cMt6ZHsAyWOdU9q1eg1JQ8MA8PBydo3v+jETgMFXxioR4gUVZVbhG+KB3tN13o+EjieSGqVlbYHgnuAZ0Gan3LBhAy+99BK7du0iPz+fb7/9lmuvvbbNzn+Kw26n2OpB3yDXWbKgrsa5lo3B4Nloe1lJMSn795OZkU5RcQkV1TU4tM43agMqesfFMOqy8UTFNn9MRUR0DBxL5URqCr0GDqa2qopj+/eRkZZCQUEhlbV1WFTqhu4qncOOr9GT8ppaTBotWceOENOj11nnPfrbBgC69RskFrHsADarmYKUVDwDQKf3bPqATip9dznGIAgKP/8q5VmHyjAYtfiFut7PqehEtahPI4ikRnGniu61odraWvr378/tt9/O9ddf36bnPlNlzmGsaAmOiG2352ipulpnUpOemkLK0cMUl5VRa7FhP9UKY7djwEGwtydhIaGMmHQl4VHRrXqumO7dYc0vfPnfFai/+RbrqQTG4UCPA39vI+Fh4c7uqn79MXg6PwiyM47z7rIPWffLL8w5R1Kza+W3AFz9kPvX2XFle9e+x+FNP1OUWoXdrCJhdOdYpbo1ykvSqciKoe/lF64obDXb8A91vXWx6mutFGdVkTAoWOlQBIWJpEZJdWVQfBRGz23T006ePJnJk9t/teHi46dmPvVtYs8OdHJSRkp+IZLdhqdaRYS/L1FRUcT37EVcz15otG0zyywgJBRPHNhlmQBfb+d4mx49SUpORqc//5ie6Lh4NHYrBQX553y8NCcLncEDw3m6tISLYzHV8L9X/kTWriKMwdB9dAK9Rkwmru8kpUNTzN4NW5DlMAZceuEvWKGxPhQcr+KXDw8zYU7vdonFYXNQml9LeX4t1WVmaivM1FWZqa+xYq61Yq63YzXbsFsc2O0O5DPyMKOf6w1gFjqWSGqUlL3deR0zXNk4Wqk4LxMdFnyjeiodSoOeAwcxrb6e0KhIwmNi23U8ikql4m9PPdOqY/VaLVbH2dNi7TYbAD4hXa8AXEeorSrig7/ehqlCxdAbhzL2uie67Mwy50xBiTWffkra5mBCEgvxCbhwV/I18wbx+cLtHN1cgNVk58p7Wl7KwVRnpSizipLsGsoLaqksqaeuwkJ9rRWryY58jv8LcDaCqtQSaq0arV6Fp7cOvVGDwajDXGclP62SqJ5+LY5H6FxEUqOk7K3gFQr+7lkbo7iknCCt68x8AmeiMWiM6680bHLI6M7xWZpz5CAANWUtn1WllKbGcD311FMsX76c7OxsdDodgwcP5tlnn2X48I5P5qvLczBVOP9ew+MGdtmE5tD2tax7TyZpbAmpm5xjaHx6+TF16tRz/h6tViuPP/44P/zwA8ePH0ev9iTx14HkFz7M7QvO38KVl1rOgXW5FByvxFRrxWZ1NLSmniJJoNGp0Xtq8Av2wDvQgF+YEb8QD3wCDfgEe+Dpo7vgF5Tfvk6jpszcMKBZ6LpEUqOkrG0QPdylF7H87rX51JltdI/rRsKQifjFnB4DUlhjI8xb/Am1RqBOQ5HFjrmuDr3n6UGX4Yk9ADC50SKWTY3hSkpK4rXXXiM+Pp76+nr+/e9/M2nSJNLS0ggO7tgxEGHdBnHT80/y3X+eYcXzS+k7eSNX3PZSh8agNLvdxrr3nJlF7hHne8/AaZVUYjjv77Guro7du3ezYMEC+vfvT2lpKbfNupcnX3kQT9VHDJzUjdjkQFDBkY15pGwvpCS3pqFIn9agxuinx+inxyfIQEC4keAYb0KifdB5Xvx7SGFGpagkLAAiqVGOzQJ5u2HCE0pHcl7mmnJ2l+jwkewc3V+MvP9zgtQ1dA82EN8zmSKrJwNCXHe2QWxsLCdOnDhr+/3338/rr7+uQESnSWo1YCcnM4OE3qerI+sM7lemv6kxXDfddFOj+y+//DLvvvsu+/fvZ8KECe0d3lnC44Zy4+Ov8M4DD3J0/UEm3mJBre463/D3bVwDOF9vfaUngQknGHr5LLRaw3l/j76+vqxevbrRtk++eo9hw4aRcjidkuyaxgdI4BvkQWy/IPpPiMY7oP3GutitDooyqxl5nfstwCm0PZHUKCV/H9hMLr2IZWn6PmRU3DB1EoFxfTm+fRVpKUc5XGhha8ERQE1YfPsMFmwLO3bswG63N9w/ePAgl19+OTfccIOCUTmZrc6xM6emoJ8iyye/2V5goLE7s1gsLF26FF9fX/r3V2ats+ryHJY/8yBqvYNpD8/rUgkNQEF6ORDK6Jst9Bt5BSp16z4GKisrkSSJOxdNoCzTSu6xcsz1VpKGhZE0JBSVpv26pa02G/UWE+pqEyXZ9dhtDsK7u05pCUE5IqlRSvZW0HhAeL82P3VNTQ1paWkN9zMyMti7dy8BAQHExMQ0+zwag3PqpsNqwcM/nD5X3EafK0B2OChO2UFx5mFihrT/LKvW+n3XxvPPP09CQgKXXnqpQhGd1qt3L7bsP0RNVUWj7afGeKjOUezPna1cuZKZM2dSV1dHeHg4q1evJigoqMPjqCw9wWdPPoC52sG18/9CbO+ObynqCLIs8/Xij0CSuPb/ZjUqHnn5zX/ggyM/sOenehL7VWH0aXmNLJPJxPz587npppuIiAkhIgb6XhJJ7sYDHPnvOrK+da4kcuoC4HDIyA6QZeeMJedtGYcsO/dzND7GgQqb1YFZo6bcx4Dk0KC2a1A7tKjlxv8fGoeZoCivc0QqdDWuM8Kzq8naCpGDQN32xdV27tzJwIEDGThwIADz5s1j4MCBPPFEy7q6DH7OpMBUU95ou6RSEdJzOH2uvN2lBglfiMVi4eOPP+aOO+5wicGhY6+YjORw8NvmLQ2tMwBFJzIAsNTXKRVau7jsssvYu3cvmzdv5sorr2TGjBkUFRV1eBw/Lv07tcVw9bz/67QJDUBJfjqFx6IoPBrJx09/SW1VZcNjWr2euEFW6krC+O7N71p8bqvVysyZM3E4HCxZsqTRY7u+2EtqqR95xRoKStQUlUoUl0mUlMmUV0BlNVTXStTWS9RbJMw2FTaHGgcaUGtQ6TRoDBp0nlqMXmqMFScILjqKobsZz/5mjMNNeI+rQ+p5gJ5HPiDCYz0DK39ipHkVKrV7vBcJ7atzfR10F7LsLLo38JZ2Of24ceMafVC2loevc1pxfU1lE3u6vv/+979UVFRw2223KR0KAJ5GL3w1EhUOMNXX4eHpbBULiooBSULv6XoFzi6G0Wike/fudO/enREjRpCYmMi7777Lo48+2qFx6I0eQCU+gZ1vJWeLpYb6ajPbflpJ6sZotJ6VRPQp5cSOeDIOH6HviNNd3flpFlQaE71HtazwpNVqZcaMGWRkZPDrr7/i49N4TF1NvZoIQwnXvHbx721H1n0L931KzoPX8uADp8dlORwOfpkyAhVmprzwFllXXo33lVde9PMJnYNIapRQngG1xS6/iKXW4IEGG6Za95mJcz7vvvsukydPJiLi/CXgO1p0dDQVJ3LQaE+31qnUapBlzHW1FzjS/cmyjNls7tDnNNWVUpiST2C8geCoPk0f4EZ2/vI92748OchciiQkKZ+JN49HrZX4aMdhrCbn/7Cpror1X/1IVV4EQ64z02/M+GY/x6mEJjU1lbVr1xIYGHjWPnWyJ2G+9Rf9ehx2OwXPPoctUs+4e59u9NiWj14iKqOaqhf/jKqmHmteHh59O9fvU2g9kdQowY0WsTRIVurdvCvkxIkTrFmzhm+++UbpUBpRqU8toHl2d5jBy5uS/Dw+e+EfRPXszTX3POCyC1teaAxXYGAgzz77LNOmTSM8PJzS0lKWLFlCTk5Ohw7YLsrdw7cvPkZdGUy447YOe96OYLXY2LVCBsnGgCn1JA7qQUjERGRZ5tjKnwEt27+ycGzbR1Tk+eGw+tFt6AkGj5/d6DwX+j1GREQwffp0du/ezcqVK7Hb7RQUFAAQEBCATqfDWmvCrPHCJ+Ti/053LPsnYdk1lL/8F7Sa0wO5TTWVSEs+Ir1fIFdPu4ea9esBMCS3vAig0DmJpEYJ2VvbfBHL9uKhdlBv6thv1G3t/fffJyQkhClTpigdSiMHj6WAVk9dbQ2+fv6As2kdwFRby7K59wBwPD+bgiunEBEbr1isF7Jz504uu+yyhvvz5s0D4NZbb+XNN9/k6NGjLFu2jJKSEgIDAxk6dCgbN26kT5+O+3b9zaLHMFXJXPf3R4jrO67DnrcjlBUex2b2ZPC1ZkZceQ0AdpOFNc9/RlqBs3vJZvOgNCOS+JH5DB4/hpDoK846z4V+j0899RQrVqwAYMCAAY2OW7t2LePGjaP8aBZIKvxizm7BaQlLTRXymx9zdEAg106+s9FjG16YR1iNndAFzwNQf/Agal9ftFFRF/WcQuchkholnCq65wY8NFBvtrbomJKcIr758AvsdjseWgOeegMeBk88jZ4YjUaMvl4kDu+Dwbv9a7I4HA7ef/99br311kYzQFyB4+QK3LXV1Q1JTXW5s5KwbHdO+R4+5x62fbiUwuxsl01qmhrD5QotZNH9Yzm65gRVpTlKh9LmQqISMIZsJm27JyOudHbtrX3pUzIKwrhiYjlhYy7hw6f2khSexcRb7zzveZr6PTY1Tq88zdly49f94rp4Ny/+O4E1NpIee6bRoP7C44cI/XYzGVf24ZrkMQCYDhzE0LevSwz+F1yDa7Znd2b15VB8xOXH05zioVNRb7E3veNJ1cUVfPTeMqottQT4+oMaSusqSC3KYNvxPfx0YB1fb1rJ2i9WtV/QZ1izZg1ZWVnccccdHfJ8LTHrZPfLRx980LBtxWefYg4Kx+5h5M4ly+gzfCQABzb8qkSIncYVt/2TwO4afnnzM1a++WCbDKR3FZKkJizRSmVeENUVZWx85VuO5cZw2bhquk+/nspDe5BREzu4davRN1dlThmSbMc/qfWtJtUFOfh8+SvHxifQJ7nxeJ+9T/2ZOoPEJY+9AjiTrPqDBzEku9CCuoLiXOura1eQvcN57S4tNTotJXWmZu1bX1XLh299gMVh5fbZtxKSGHnWPjaLjTeef4VjuWn4fPkLo29o32m1kyZNctkPsKS+yfDV19TbnV1ONVWVpBeX4RMSwUOPv4larW7ojio+dkjJUN2eRuvJzU99zucL7+bY2gzGXJ+BX/D5W75kWaa8OB1LfTVh3QZit5koyTuMX3Aieg/XK8dv8PQD4MP5ewE/esbk0eNG55iZ/JQStJIPCVdMbNcYqkrqMdgcqPWtL1Ox9bl5+CMzYn7jpSuO/PoNMduzyXxwKv4BzpYgW0EB9pISPMR4GuEMIqnpaNlbwRgMAa7ZlfB7Rk8PjpbYKTy8hdDeI8+7n81k5ZPXllFhrWbOH246Z0IDoNFpiA6MYG/xUbYe3sVoOm+tkJb6+M0lIElcdvnlqE8OIlapVKD3QLY0L7EUzk+jNZA0chwFR7+lrPBgo6TG4XBwaPNyju/5jbKcAqoK6rGZnA3Zeh871joJh01F3MhQ/jD33XaL0WYz8cPun1m5Px+Lw4NIHws3jR5Hn+juFzxu0GXjqTr+Bjnpw4kLTmP83+5pWFPuRLqdSP9CJG37VqmurrLhqXK0+vj8I7sIW3OA9BnDGBJ1eo05h81G4XPPYonSM+HefzRsrz/oXPzV0FckNcJpIqnpaNnbXX4RyzMNv2I6qe8t5e0vVnL1oH30vuJ2dHp9o30cNjufv/oheeZiZl5xPdH9L7wGy7UPzET/9jccyktpz9DdgmSzIp0cP1NQXQtqDX2HNJ4Vp/f1x1xVfq7DhRbqd+mNbPv6a9Z/9CFxz09FkiQyD6/nl/cWU5FtRedtwzfcg4RRcYTF98ZuM5F7NAXfkDD2/7iTysL2WT09Ne8YH278jR+O6imt9yPQw4/SemeF3G8P7uPWodtJDA3k2mETUanUZx2vKf6Vq0z/pLJ3NH73bUXSqJFlmZVPfE5BTSTjxpS0S9xnqjVr8TPaWn38gYXz8fRWMf4v/2q0fds7iwjNqaPilUfQaU6/95gOHEQTHIw2NKTVzyl0PiKp6Uh2K+TshMv+rnQkzeYT2YO75z3BF28u4r+7C8jPeZLJ9z/f8LjD7uB/r31Oak021469iqRRzevf1mq1WOXmj9XpjGoqK5A1WgI8Dfz0xWeg1oAso9U1XovIw98fSyeoFeQK9B5ejLzpcta9+Qu//fdl9F4aNr6/CoOfzIQ/Xke/S+46e+r8yZVAik7cRE1JzdknbSWTxcSXW3/hy10F7C8Mw6Dx5bKEGm4Z1Y2RSX2orKvmq61r+Pc6mTe2+AI2Cqu+4Y+TGk+FNxftQfPVvdR7e+F75xrUOmfhxhM//0JWcQjBxgISr72mzeI+l5JdR6nFm2j/1v18UjZ8R/SuHDIfmoaP9+nlM+orSlG/vZwjQ0P4w6TbGh1jOnhATOUWziKSmo5UsB9s9W4zSPgUrdGXmXOf5ZOXH2VnkY7Bh9ZjNpnxDevGti/WsK+ymEmDxjFg4rDmn1Orw9bFk5q0w85xMtV19Ww9fAzJbmPWjBln7ecdGEx56tFznqO+toadv66hsqSYq26902Vr2biSQePmkrJlK9uWrwUgtJcfNzzyVpNjZaz1NrSGs1tJWmNn2l7u/OgYlWYvegVJPH65jRtHXYm3x+lK0n5GH+6a8AduG+cAHFz98odsTLXyx0mNz1W/8k58bHYcs1eiMTqrgMsOBztW5RPu5eC6F29p1XImO/YW8PPnKahrbVxxd18GJjduETnx806qcsrJ2F1AjjUMDTLdx7d8gVtZlsl+fiFyuI4Jdz3T6LHNz84lwOKg/4KXzjqm/uAhAm+/rcXPJ3RuIqnpSFnbQK2HcGVWJ74YarWamff+lbf+8xJLvlx7cutmAIZKPoy6ZlyLzqfTa7FLDhwOR5f9IF7z/feg0WHR6PDXa7jjj3Px9vM7a7+AsAiyHXYyjhxCrdFSmJ1F6q7tFKenYK0oRTo5EPrS66bjfXJquHB+kiQx9f5/8dHf7yW8ZxRTH3gddRMrVcuyTG1ZNQHRwRfcr7lKqqupNHvx0jUSN4y88Mw8jVoFqAg0wu68AP713YfcM2Ea3icHB0tD74HsRzCvmoth9lqQJHLW/kpRXSRXT6dZCc3hlFJWfpWCI78efZwXOouMOasWlRpkYPXSg/R4aSyeBucg4Izvt/HjiipkSY3O6sPgvmYG3jUBXSvKNOz88nUijldR8twD6LSnu5cKjuwm5PudpFw3gOuTGn9hsmZl4aiqwtBXzHwSGhNJTUfKPrmIpUbf9L4uSOcTzK1338+uVZ9RUFLOsWpPvGQVQzxb/kav1Tq7WCwmMwbP9q9X44oCAwOoqXQ21z/w8CONlks4U3CUcyruN0890rBNVmvwCAkn+tLLyT98gPrSYpHQtICXXxR/XPJ9s/fPy9xMXYmaYdeOaZPnH9NrMGppNdmlze+uuX/CCBb8dyev/hZIed13LLzRub6Sb/J9pB3bz7a1I+hZ9wYDbruLrd8XEOQhETN+VrPO/e3SA+hr7Vh9NahTqgEJPTD1b4OoqDSz/Y1DvP/ufkYOD0F7NI1tayvxV9Ux+W+X4Bnsi86vdTPCbGYTplfepqiHD1dd90Cjxw49+TBaHxXj//rvs46rP3BqkLBIaoTGRFLTUWTZ2VLTf6bSkVwUn7BYLrvVuQhhybHt1Hy4G6225X9GOsPJpKau6yY1N959L19+8D79Bw85b0ID0L3fAH6L7IbeywujXwDJY8fRY+AgNCeL97186wxUnWwBTFdzcMPXSGoHfUbf2Cbn8zJ40i+0hF9TVMxr5jGje/Tm10d6M+3ld9icocZud6BWqyjJP8GvmyditfuxdTdkHX6XElMC180xNrvbSaVXUyfLPPH8pdhtDqxWB3q9GknlnNCwJjgFnwOVbDngXNzWKFuZ/LdL8TvPLMfm2vTW0wSXWPB96R+NCugd+f4TIvbnc/xv1+PvF3bWcaYDB9BGRaHxF4m80JhIajpKxQmoKXC78TQXEtRjGA7VsVaVcNSdHAxrrnfvJRguhqeXN7f+35+a3M/o7c39L79+3scd5nokUx2fvPQsNz38d1FdtR2c2JNCcHdvDJ6+bXbOy3saeGmdD4WVlYT6Nv+8d47pxkPfWFh/9CCX9OjB92+uB7yZ+VQCO19fzfHieCZeaSFs1KQmz3VKeKIvlVuLKSipIyzIE7Wm8T/1X54YxaG0MtL++iRanYNpXy1BpVJhs9rIOXKckLhIPL1blljXVpbgsWwFaSMiuWbktIbtDpuN4pf+RVWcB5NufeKcx9YfEkX3hHPrmoMZlHBqEUs3KbrXXLKsQmrF2Emdh7MLzqJgUuNwONj/wzbSdxxtKHLnjiY9NB/Jy4f8XVuxWVu2pIXQtOLc/VTnQ/fhbfuF5Nohw5Akma+3/9ai464aOIJAQzl3fJTNX574gpqiCMbfFkdgWDcuf+JW5ixIJvHaq1t0zoHDwtEgsWVL7jkf12nVDOwVjJc5H31FHpaT68H98KcF1M+4huPDhrH965ZVCd/00l8xmBwMeOyFRtu3LnuJ4IJ6Auc9hE6tO+s42W7HdPgIHqLrSTgHkdR0lOxtEJTkFotYtoQsa5Ba01JjOJXUKFNUzm618+UrH/PN9h/56Pvl/PCO8usTtZZKpcJRW41vfI+zpoMLF2//+uVIKpl+Y29u0/NGBCWSHJzHTwdbVkNGq/Vm8YwEgrHQvSwCj9gyuvcbAIBKq8UY2fK1l7b9losdmQH9L1zzRX/d9UQWHGfT5dP4dcnHxK9bQcrYq1HLDsrTMpr9fIVZRwn931ZOTE6mW+Lghu1WUz2Odz4lrV8gIy+/9ZzHmtPTkevqRNE94ZxEUtNRst1nEcsWkdWgbnl3R8OYGpOlrSNqkqXOxMf/fpej5RlMHjiegcG92Jt7GIfdPVtrUvfsBOCWBf9oYk+hNTJ2HSQgzoDRp21mPp3p8l5GDhT6U1jRsuKKY3uPZMWfL6FeJVOfEcQbr+/C3oq/332Hi3l23lqsu8ooC9ISF3PhbrDx99+M+s33UdtthL/yLIX+4Ux+dSH1Gj3m3LxmP+/O5/6KRSMx9pGXG23f+OaT+Jfb6P7Xx897rPmYs2inoVfPZj+f0HWIpKYjmCqh8FCnTGpk1BfZ/dTxSc33739LVm0B1186leHXXEJcj3hskoPKAves2pt7+AAABo+uOeC6PdVVllOZYyducPu0Cvxh2BgkSea/Oze1+Njw0EAuvbMPFknGcaCS33bmN3lMaaWJ/cdK+OrHVJa8sZtVHx/Fr06mwiAx8Q8XXorhlD6XDGXYqu9Iv3o20f/+FzqDnuwRE4n+5X+cOJja5PFpe9YRuz6Nspnj8Q8+vfhlXXU5np98z/HhkfQaeuV5j1ednFggWzr+vUNwfReV1CxatAhJkpg7dy4AVquVRx55hOTkZIxGIxEREcyZM4e8vAtn8G+//TZjx47F398ff39/Jk6cyPbt2xvt89RTTyFJUqNLWNjZo+JdUs4OQO5Ug4RPaXX3k6dzHRqLpWPH1DgcDlKKM+kf1pO+4wcB4HHyTbK+urZDY2kLZUVFmAvzGmrVCC3jsNvZ8dObvDzrav5149X8Z840juz8nP++di8fP3UjSx+cDUgEx1x46Y/WigiMI8q7kr0nclp1/PDB4Uz9q7P75sjB4gvum55VyfJHNrPx3/sp/F828r4KjGVWavTwyEuXMGZQ87utjL5eXP3Px0ka7qy5NfhPdyJLEvsfP39rodVcz661y0n5+18o89dw2Z8aj6XZ+MrfMdY66Dt/4QWfWxPkrDhsK2n/pR8E99Pq2U87duxg6dKl9OvXr2FbXV0du3fvZsGCBfTv35/y8nLmzp3LtGnT2Llz53nPtW7dOmbNmsWoUaMwGAy8+OKLTJo0iUOHDhEZeXrKYJ8+fVizZk3D/VOL/rm8rG3gGQiBzfsm5F5a1/20d/MuAHy9fdo6oAvK2p9OPWZ69ndWPjXXmfjp19UYMRDcLbRDY2kLHkbnjJPuE6coHIn72b/pQzYv/5LaYhmDnxVThRab2cEPL32ExsOB3ksFsvNv2z+oR7vFkRAkc7y09RNRI6O8KPYAr91lFF1TR0iQZ6PH04+U8tN/9mGRZHRImCINTLo+EZvNwdofM7j1jn5otK1/L133ygeELnkBHRLq/gPPuU91SQE7br2O8PQK4oDix25Ff0b15MrSfAK/XE/mpd2Z2uvCX/6kky2Sjlr3+xIitL9W/SfV1NQwe/Zs3n77bRYuPJ1V+/r6snr16kb7vvrqqwwbNoysrCxiYmLOeb5PPvmk0f23336br776il9++YU5c+acDlajcZ/WmTNlb3WrRSxbQpY1SJqWNdUUpObwy+4N9PGNJ2FEy8uqX4wjuw6ilzUkDHX2x6/9+EcqrNXcdu1stB7uVxTRw2hEllRk7tqK4857u2x15pbKTd/O6lc/xydKxeS/3ESvobOQJInctB1kHd7MkCvvQavzwGqpozjnAOFxzV8CpKWSQj3YmuWJ3W5FrT5/vaLz8dBpmPngQFa+tJsvH99KrRbG3dmbYQOc75U/fHoUFaCTJXrOTGDCuG4Nxw7sd3GJvN1ipWjXXkIBz8+/5ar+507+Nv3rb8SmV5Bz/1R6TZhOrz6Nf55b336WMKvM0PkvnPP4M+ljY1EZjdRt347n4MFN7i90La16B3zggQeYMmUKEydObHLfyspKJEnC7xzl38+nrq4Oq9VKQEDjmUKpqalEREQQFxfHzJkzOX78+AXPYzabqaqqanTpcHYb5OzqlONpHKVFyHggtaClxlpv4avlX+Kl8mDqXdM7vKZKel4m3XwiUGs1VBWVsyv3IAOiehE9sH26FzqCZPDAVl5KYXaW0qG4jfQ9P4MEs59+j97Dbmr4O4zsPpSR0x5Cq3O2Bmh1nkTEt/3/rslqJa0gj/zyUuKC/Ki3eZBR1PzZQ7/XM96f7lO7YZZkjFb4aUXa6ccuc7Z297o5sVFCc7HSD6SyY9hokretIs8/gvjzJDQAhm0HSR8WyeV/epGoPmcniLaSYqp8NYR1a/pLjqTT4XXpJZR99DHV69ZdzEsQOqEWJzXLly9n9+7dLFq0qMl9TSYT8+fP56abbsLHp/ndDPPnzycyMrJR0jR8+HA+/PBDVq1axdtvv01BQQGjRo2itLT0vOdZtGgRvr6+DZfo6Ohmx9BmCg+CtbZTjqepX+9c+0kb2/y++BXvfEm5rZrp112PwcfznPs47A5KThRyeO1uSk4UtkmsAGW5JZTYK0lKcr75/vbTegDGXdv8ImWupLKgjHWffAP1zmb42qpKhSNyH7nHjuAVIuHp1fYzmprjoWXLmLh4DyNf2Mr875wDXtMLmj976Fyuvao7c1+/jPoQHfbK0/WKsvKcK7x7era8FehCcjZtw9dUTeot/8eY9eevUSPLMr6lJnzSCrDbzq6jZHfY0R1Mx9yC+EIffRRD3z7k3PdH8v7+GPYa0RUlOLWo+yk7O5uHHnqIn3/+GYPBcMF9rVYrM2fOxOFwsGTJkmY/x4svvshnn33GunXrGj3H5MmTG24nJyczcuRIEhISWLZsGfPmnbvQ+KOPPtrosaqqqo5PbLK3gVoH4QM69nk7gDW/CrXagX7EH5q1/86Vv3GgNJXL+44lul98w3a73c7mr9eSm5tDaW0F5dZqbJJzBe+EnZHc8te72yTew1v2IckSvUY5x4EdO5FOd69ovIPbrkpsR/j14x/YmbaPOtmEZ+ZR1EBQ8iCiE5OUDs1tlGaWE9FHua7s+CAZ0uCFqQ7MVgcgMbbXxXdxqVQqjEEGrCVmZFnm9Xf3Iu8swxykY0i/C9egaamEaVdS/soivFUyet35P0oyi1LwMMt4mO1s/+ZNRs54sNHj23/+kKjMWgrvv6bZz60JDib6rbeo/PprChc9j+nAAaLffANt5MUt2yC4vxYlNbt27aKoqIjBZ/Rj2u12NmzYwGuvvYbZbEatVmO1WpkxYwYZGRn8+uuvzW6l+ec//8lzzz3HmjVrGg1APhej0UhycjKpqeefQqjX69HrFR4nkbUVIgaC9sJJoDuylqnQGqubvf/m3VsxoGPA5Y3fvHf88Bu/HN5EiMafYK8AegUlERIRxvat2yiqK+OrVz7GZDFzwx9noze2/ueYmp5KhC4QY6A3xSfyqbBXM67XqFafryPYLFZStx3m8P5D+Hr70HdEfzam7iDRNwbP8nLSTbVcd/fjxE/sfC2B7aW0IAVzlYroXgMUi6HKpMVPX8ONbbSW1JmCI7xwHK5i8448pJ3l1BvVzHtq1FlLH1ysqMgg9nXrjeOXX+DR/zvvfm/84w/cDmRO6sMlV9501uNV6cfwA6qONz0d/EySJOE3fToeAwaQfd8fyZhxI3Fff4XWHcddCm2mRUnNhAkTOHDgQKNtt99+Oz179uSRRx5plNCkpqaydu1aAgMDm3Xul156iYULF7Jq1SqGDBnS5P5ms5kjR44wduzYlryEjpe9Dfper3QU7cJa54cxrvm1Xa6+cgqf//A1776ylFvuvA3/yEAcdge/7d5CgmcUt/ztrkb752flkpuxh6ySXKpU9RRn5BPVN65VsZprTeTUFTGm+1AAio47a3oExrX9jCdZlqkpqSQ/NYfC7HyKioupqath2uw/4B8Z1KxzVBaWs/XHjezLPEQdJ6e9F0NWXjZGSc9VMybz0d8eJCF6sEhoWih9n7OrJH7AFYrFUFQjY3O0z3iywcMj+PqXXLYtO4Ye0EYbUWvaZ6aoceJEgt75D/kn8gjvdu5u6BuumQ9rniPq2hsx+pz9eeBh8ALAOzK2VTHou3cn9ovPOX7NNRS9+CKRL7/c9EFCp9WipMbb25u+v1tvw2g0EhgYSN++fbHZbEyfPp3du3ezcuVK7HY7BQUFAAQEBDQsYjhnzhwiIyMbxuW8+OKLLFiwgE8//ZTY2NiGY7y8vPDycv7BP/zww0ydOpWYmBiKiopYuHAhVVVV3HrruUtpu4SKbKjK7ZTjaezFRThkX7QtaO2NH9aT27xv4ZPPP+Xdd97h5lk3UVtZS7VczzWXnN1icvltU5ngmELBsWyWfv4+5rrW1bTJOnCc1d//hF1y0HuYs4hadJ84PNfr+fyrL7nL/y78IpqXfJ9JlmWqy6ooSMuh8EQeRcXFlFSVUWauxIxz7IBKlvBVGyl31JC+5xhDLpDUyLJM5v5Utqz9jbTyLFRI9AyIp3f/PmSkHGdH3gGy64uYNuIKNr75LrLs4LI/3d+qn0lXlnN4L3pfB4Gh7TdN+0J+ObCHX9IDuX1wUbucPy7ah94zEsj4/DgmSeamW/q0y/MAhA7uD+/IbLp7LtP+9yF6j7NbUgeMm8Fuz0WU/fQtyeNvOOvxyq2/EQz4D2z6y+z5aAICCPnLX8if/yiB996HoYfoiu2q2nSV7pycHFasWAHAgAEDGj22du1axo0bB0BWVlajqadLlizBYrEwffr0Rsc8+eSTPPXUUw3nnjVrFiUlJQQHBzNixAi2bt1Kt25tN5q/zWWfXMQyqv2mgyrFevQoIKHt3rKWk/BeMdx+5x18/P6HfPDph4R5BmFAR9yQc3/AqFSqhi4ni6l160T9uPIHasy1XJk8jtAezgqmPiH+3HnrHSxd9g4b/ruGafefvxtAlmWqy6vIT8uhKCufoqIiSirPTl78VF4EevoRGxJNSFgIYfGRBMeHo9FrefbJhVSWVZzz/FaThT1rtrFj3y6KrRX44MnYhKEMu2oMxiBn123PS/ozuuAyPHw9KTx4lF9TtzBq9I34xrhfbR2lFWcUEhjrp9jzL/z+IAl+Zv52zex2e46rLotlk6eWkGBPwgPPPSC/LUT0SSRTpaFv1gH2bjnA8PFDz9pHq9VTODAa362Hz3rMXFWJMd35Jdbgf3GDtn2uvJL8xx6nfs8ekdR0YRed1Kw7Y0pdbGwscjMqm6773TS8zMzMJo9Zvnx5CyNzAVlbISABFJph0Z6sJwqR8EcT3/I3j8DoEO68/x4+evMDMuvy6e2fcMHmcYPROb12xa8/UF9dx6ApTY+DcTgcFKXnkbrrCCXmcoZG92PE9eMaxxEXSnxgNClFGVQVVeAd7EtVRRUFx3OdLS+FRZRWllJqqsLy++TFw4/YyChCwkMJi40guHsEGsP5Z294qz3OW1Jg+Ssfkl6XQ4wmhOtHTKHPhEGoflcMTaVS4RcRgN1m45e3l+DvGc6we2c2+XMQGnPYbdQUy8QObPmij21h/4mjZFQE0M23HL22+ctabNiwgZdeeoldu3aRn5/Pt99+y7XXXtvwuCzLPP300yxdupTy8nKGDx/O66+/TlJ8+w6c9Q0JxL5gITw9H//w87/P+Y6fQMhv75N7ZCeRvZwtMjVlRWyfPRXfEhMl//ozYwc3XSLkQlQGA/qEBEyHDl3UeQT31qYtNcLvZG/tlF1PANbCOjQ6G5K2ddNEvYJ8uOPP9/DzxysZcumF64AYA32Y2HsMmw5vJyszi0E0ndRs+Xotqw9tRCVLhOkD6XfpuYt0jZs8kQ8+/oBXl7yKhIQFGwBqWYWfZCTAw4+4iGiCw0IIi40kuHs4Gs+Wr4TtrTNSVdd4ULXD7iBrZyrpdTmMiRjIhLumIakuPM5i+wfLKavO4/q7nkCtb9spul2BSq0hKMFA6pbDjJ9di1ZnbPqgNmK327jlvX2AF1qVrUXH1tbW0r9/f26//Xauv/7sMXovvvgiL7/8Mh988AFJSUksXLiQyy+/nGPHjuHt7d1Gr+DcavKL0CMRHn3+Abr9rppD1qL32btsMZHPf0x5YRZ7b7oO79I6bIsfZ+z4tmm10oSFYisva5NzCe5JJDXtxVztXMRyaNtMR3Y11ko9Wt/WdQedojcamHrv9KZ3BMbMmMj+hQexnqPOxblYrVbUsoq/PvxXDN7n/0Yc2j2C++69j20/bgSrTEhoCKHdIghKCEPr03Yz1ryNXhRUnF6bpygznyUfvAWAJEPP0f2bTGgqcvPY9svX9Oo2mm4Tz27mF5rnspsf5MunXmLTNy9y2cynO+x58yvKqTR7ccugUv4ypfnTl8FZ0uLMshZnkmWZxYsX89hjj/GHPzjLKyxbtozQ0FA+/fRT7r333ouO/ULqi0uo0hvpbTz/TFN//zC2TRlCzIqdbB36OqZX3sajxoLujefpP7JlP4sL6oRV24WWETXV20vOTpAdnbKlRrbZsFqC0YZ07DR1rUpLdX0Nppr6Jvf18vPGLjmQbY4m9/UND2DSHdcw6d5rGXDtKMIHxrZpQgPONa6q7XUN96uLKhpuz7piOlF9Yps8x5Z3PkYr6bn0T/d2eCXmziSm5zgi+/ux/6cd1FV33KKI4f7OQeIf7Q7EYmtZS82FZGRkUFBQwKRJp4tI6vV6Lr30UjZv3txmz3M+1pJSaj28m/ybHP/0WxSHG/B97DV09Vb833mtbRMaAJtd/G90cSKpaS/Z28DDHwITlY6kzdky0gEd2m4dWw8iyCeALFMhL770Im8vep1fPvye7APHcTjOTly8fJxN7jVlrlFl19ffDzNWzLXO1i3fMOcSIHH6cJJG9b3QoQ3yMo4S060vxqiApncWLmj8LfOxW+Drl/7I1h/+fc6/obZgt9vZlrqPZ775nMtf/AiARP881FLbPd+p2aKhoY0HjYeGhjY81q7KyzB5NV3AUmfwJPblVzg+KIzIZe/TY+D4Ng/Fkp2NNjKqzc8ruA/R/dResk4uYtkJFxi0pqUD3mg7eIbBNfffyMi0XI7tOszxrAy2pO9i4/EdeHytJ9Y3goTE7vQY3hfvYF+8fE8mNRU1uMIwbd8gfwDKc4sJjo/AWu8sjZ9hzm/2OTRqPQ7Z3i7xdTUhUckMmDqIPf/dS9GxX/ht2S/4RErMfOJN9qx5D0mlQqv3QGvwJPvIHiIS+zDsij8169x1ZjPf7drAmkMn2J7jRaXZGw+NmgFhdTx4WQjXDL0dlart33p/30Ihy3KHtFqoqyqw+Po1a9/4/mOJ/3Rtu8ThqK/Hmp2NPrF7u5xfcA8iqWkPDruz+2nsuZdvcHfW7HJUkgN1aMfOIFGpVIQlRROWFM2lOKdCp+84QuqhFDKKsjiyM4OVO1YTrPUjxMtZd6amqvkVj9uTf7gznk8//pRayYz95Df1AFXzB3FKKgnap0GhSxo/ayGjr61k15q3ObpxI+Un7Lzzf/fgsAI0TgbyDuY0O6l56usv+GJ/AJHeeiYlmbi8TziX9L4Mg659plaHnaygW1BQQHh4eMP2oqKis1pv2oO+uhJLdOuKYrYl8/HjIMvoE9x3cVrh4omkpj0UHgJLdaccTwNgLbWj9Wx+JeH2ojXo6Dm2Pz3H9gegIq+EY9sOkXY8nWPlmSCBRt34Tzw3N5dHHnmEH3/8kfr6epKSknj33XcbLf3RHgKjQ+nhE4tarSYkOJiw6HAikmLwCfVv9jn0eiMZWXtZ//JSRt53CzrP5k8JFs5N7+HLqKkP4+njwy9LVqAzStzwxAsEhiViqa/GXF/JqnefpCTj/AvnyrLM5mM7yS3NJyY4nLRiCxrJxm+P3dIhryEuLo6wsDBWr17NwIEDAbBYLKxfv54XXnih3Z/fs66K+qCWF69sa5Y058rkuu6ipaYrE0lNe8jeBiqtc82nTsha44VHRI3SYZzFLyKI4dddynAuxW6zU3qikKDY0+N+ysvLGT16NJdddhk//vgjISEhpKen4+fn1+6xqbVqZs277aLOMfXJx9j42rvs3raSI3vWM3razfS5/opGhSyF1uk9/CZUkid9Rs9EfTIR9vAKxMMrkJC4eHL2lWG3W1CrG0/nP3jiCH//5jf2F4YDaqAICEOvNmO321Gr22Z5gpqaGtJOfmiDc3Dw3r17CQgIICYmhrlz5/Lcc8+RmJhIYmIizz33HJ6entx009lrLbUlk8mMt7mWmuDmLf/RnqwFhUgGA1Ib/cwF9ySSmvaQvQ3C+0MLimu5C0dVJXZ7MNpw1/4gVWvUhCQ07h574YUXiI6O5v3332/YFhsb28GRtZ5nsB9XPP0XBu2/hl+XvsXPX73O3jXfM/6u+4gc2rzBxsK56Qxe9Lvk5nM+FhAeB45dlOYdJST69EK75TXl3PnhLsDA81M19IkMpaLOjLeHJ5EBYW2W0ADs3LmTyy67rOH+vHnOru1bb72VDz74gL/97W/U19dz//33NxTf+/nnn9u9Rk1hThEqZLxClR+55jPlKopfe42yZR8SdF/7TmMXXJckN6cEcCdRVVWFr68vlZWVzV45vFUWJ0OvaXDFs+33HAox79xO8VdmQmZ6ohvQvl02ba13795cccUV5OTksH79eiIjI7n//vu5+273qyUkyzIpK9ez8atlVJqKSYobzqUP3otPZIjSoXU6ece38dmj/8AzyIHeqKX3pZcybPJcbnvrPbbn+PPtH/vSK0qZdaSUtnvdTjzuuwVpyXv0HD9S6XAoeO45Kr/+hoTVP6MJELMEO5Pmfn679tdtd1SVDxVZEN351nsCsGZkA3a0Sb2UDqXFjh8/zhtvvEFiYiKrVq3ivvvu409/+hMffvih0qG1mCRJ9Jg6jtveeotRl9xI5ol9vP/wfeRsO9CwT31ZJWk/b2bTf97j63mP886tt3Pwy58UjNo9hcUOJfmqXgTGBFNTbCVl22Ze/elLNpwIZ8EV+i6b0ABU5jqnjAdGucYaZEF//CNIEiVvvKl0KIJCRPdTWzu1iGX0hUv/uytrfg0ajYzk2X6L5LUXh8PBkCFDeO655wAYOHAghw4d4o033mDOnDkKR9c6GoOOkQ/cQvL0q3hv7j1sXPY++s89KSnJotrsLBevlrQEeEdQYy6nKD1d4Yjdj0qlYtKtLwGw6P9u4WiOHz9s8GJKj0JmX3KHwtEpJy81i5qPP8ImqQiIdI2kRuPvT+Ddd1P82msEzLkFXXS00iEJHUwkNW0tezv4dQPvji1M1x5ksxlbdiaa2AQkjfNPxVquQetdq3BkrRMeHk7v3r0bbevVqxdff/21QhG1Ha/QQPx9wiksyyDAO5zo2L6EdU8kvF9vgvsmoNZoWHLzLH4/VVloGbPdG08dvDHDh0n9r1Q6HEXYrDZWP7+EkM/fI0ijo+LhJ1EbO24NraYEzLmF8k8/pfjfi4l8+V9KhyN0MJHUtLXsrZ2mlab6k6+oSolBIgutRxG6ADuW+jh8orOVDq1VRo8ezbFjxxptS0lJoVu3bgpF1LZuen0xOECtO8+/dZcZPdeeVHh5ejF50FilA1HEoY07yXn8CWILM0gZNpFLXnoK/1Dlp3OfSeXhQfCD/0f+4wsIuP02PJKTlQ5J6EBiTE1bstZD/r5OM55GrnWuVeTTqwS1wUZ9gXMmha6Hezbp/vnPf2br1q0899xzpKWl8emnn7J06VIeeOABpUNrE2qN5vwJzUliWZyL1EFVel1NTUUV//vjo8j33IrWYsb08htc8+GrLpfQnOJ77bXouidQ+MILmI4exVZeTheaE9OliZaatpS3Bxy2TlN0TxMZBLngcfmleEc4p0fLFjOS7vyr8bqyoUOH8u233/Loo4/yzDPPEBcXx+LFi5k9e7bSoXWQrvmB3JZkWUbVxX6Gv32yAvu/X6RbXSWZU2dz+dPz0Ht07GK2LSVpNIQ+8gjZ9/2RjGuvc24zGNCGhqIJC0MbFnbyuvF9tZ+fS/2PFFWbKKoy46FTE+nnwW9pJWSU1GK1y/SL8qV/tB9eevExfibx02hLWVtB5wUhvZve1w2ovZx/HrL99D+5uyY0p1x99dVcffXVSoehCBlEU81FqKyuRVVfjeSnfKG5jlBwPIdtDy8g6fBWMrr1IfrddxjQv6fSYTWb19ixJG5YjzUnB2tBIbbCAqz5BdgKC7BkZ1O7Yzu2omI4Y8V0Sa9HExaKNjTMeR0WfvL6jMTH37/dEh+LzcFvaSWs3J/P+pRiSmrMp2OTQJbBQ6tGJUGtxY5KgmFxAdw+Oo7xPUPQqkXni0hq2lL2dogaAqrOUdFSHdcdKMF2dD/a6PAm9xdcnYwYKNw61TV1/POv8/GyVDN8SudPinf+7xdMTz1GuN1K3gOPcuUDN7tl5WpNYCCawEA8+p/7cdlux1Zaiq2gAGtBAbaCQqyFBdjyC7Dm5lG/azfWoiKwWhuOkXQ6Z+tOaCia4CA0oWHo4mLRx8Whi49HHRDQqqRnc1oJf/lyH/mVJuKDjdwwJIr+Ub5E+nlSUW8hs6SWkQmBdA/xxuGQSS+uYeeJcr7alcO9H+1Cp1HRL9KXBy7rzmU9u269KpHUtBVZdk7nHuZ+hdzOR9O9J1rtF1RvtmOYKLou3J2MaKhpDbPVyr8eeRSvimwG3/1Xxl3SOcbMnUtpfgkbH36SHrt+pTQ8gehXXmZYcpLSYbUbSa1GGxKCNiQEj379zrmP7HBgLy3FWlCItSAf26lWn4JCbCUl1B86hDU7BxzO1WZVXl5ow8NPd2uFh6ENC6fKJwBb3wFEBnmh15z+4mu1O3h5dQpvrk9nRFwg7902lJ5h3me9345NPF21WaWSSAz1JjHUm1nDYjiYW8mOzDJ+OljA7R/sYELPEBZc3ZvYINeZldZRRFLTVkrTob6s08x8ApCra1DprZhrIsFiAb17dz11ebJoqWmNN177AO+SdHrc9jATJ4xWOpx24XA4+GXJJ3i98yrRdivZtz3IxIfvRa3pHK3OF0NSqdAEB6MJDsYj+dzLkTgsFqwnTmDOyMBy4oSzpaewENOhQ1h//RV7qXNB1P8MmM6quBGEehuIDvAgyt+TtKIajuRX8bcrenLPJfGoVS3/H+0b6UvfSF9uGxXLTwcLWPj9ESb9ewN3XxLHA5d1x7OJCQSdSdd5pe0teysgObufOomKN7/GXNMN/yHlSCKh6RxETtNihWlH0RiCuHryOKVDaRdp+1M4/LfHScw8QHqvYQz510IGx7vnDEelqHQ69ImJ6BMTz/l4SWYOxVdeztjBCYwf14/s8jpyyuvJLqtDp1Hx9R9H0T/a76LjkCSJycnhjOsRwhvr03lzfTrf7M7l71f14up+4V2itV0kNW0le5tzgLDBV+lI2oylyhODbyHG6TOVDkVoAzIykshqWkwnW9E4zE3v6GZMJjM/Pv0KsSs+JtDgSdXjz3H1zdcpHVanlJeWjRboN6QXyUPbP2H00KmZd3kSNwyO4h8rD/PgZ3v4bHsWi2cOIMTbtWeuXSz3G/nlqrK3d5r6NADIMrJdhSZQ5L2dShf4ptbW+oyfjKelms8+X6l0KG1m+0+/sX7CVJK+fZ/80ZMY+MsqhouEpt2UpmUAENkroUOfNzrAk6VzhrDsjmGkFdVwzWu/cSCnskNj6GgiqWkLdWVQfLTT1KcBQJKQ1HawmJSORGhLIqdpsSsnXwZARWmJwpFcvNyKCh5/aD6ec+9GK4H2zfe4aulLePqdf9Vj4eLVnsimVmtQrFjhpUnBfPfgGEJ8DEx/czP/25urSBwdQSQ1bSFnp/O6M7XUABoPM9YyUYWz0+ii1XAvVmZ6JgCxid2VDeQiyLLMM6tWM/alNUgnKqjXqbj0l5UkjetEX8RcmC03h3LfYEX//0J9DHx+zwimJIfz0PK9vL3huGKxtCfRt9AWig6D1hP845SOpE0ZEryo2OuPo7QIVWDXrXvQWTjnPomkpqVsVgsAWg/3nB679UQmDyzfSGl5ABFhpfS51BOvo3Zq68rx0bvG6tqdnbYwn/pA5X/WBq2af83oT5ivgWd/OIJaJXHHmM71uSVaatqCXwxY66CuVOlI2pRhzHBAg2nTRqVDEdqEaHVrjVNrBrlbI1ed2cJtn37FzDf3U1knMfdqNb89dA/JJxfjzD68XeEIuw6v8iLksAilwwCcM6T+ekUP7r00nmdWHubDLZlKh9SmREtNWwg9Wbug8BDEX6psLG1IExWJVrcV05FSPK9ROhqhLYjup5Zzx1Twk907eWZFCmaTkUE9ilk6YzpBRufMzKjew8gDilP2w+ipygbaBdisNvyry6iLcZ1p8pIkMf/KntjtMk/87xBqlcTs4d2UDqtNiKSmLQTEg8bg7IbqREkNgCEWalKicFSWo/L1Vzoc4SLIbvnxrLxTPzV3WMgyq7ycuz79gZRsP7x86nl5Zg+m9Gz8jcQ3OJIUT4najFSFouxa8tOz0Mp2fOJilA6lEUmSeGxKLyx2B0/87xB9I3zbpFaO0kT3U1tQayC4BxQeVDqSNucxNAkZb8xbtigditAW3OCD2fWc7H5SOIoLsdsdPPnjKsb981dS83RcO6qaPY/MYUrPwefcvzLEiCOr886AcSUFx5wDckMS4xWO5GySJLHg6t70ifDhz5/vpc5ia/ogFyeSmrYS2tfZ/dTZ1JYBYC+vVjgQ4aKJxZ9a5eSQGpf90W3OOM7Qlz5h2XobESElrPzTMBZPm4lWrT3vMZaIIPR5nWsMoKuqSMsEILKX6yU1AFq1in/fOIC8ynpe+PGo0uFcNJHUtJXQPlB0FBx2pSNpU9rBw/HwTqX6gE7pUISLJrvsB7Mrk120167GbGbOx19y09KDVNfDw9O0bPzTPfQJbXpshCYmGv/i+oZB0EL7MWVlU+7hi6eXp9KhnFdCsBcPT+rBR1tPkFLo3l9gxZiathLaB2z18N4Vzvt2C9itJ69P3kYClQa0BucUcJ0X6DxBZwSt8QK3T94/122tR7t+hZS0BiQvL1T17v2HLrhDJ4prOvWx70oJ4bId23nu+3TMZk8G9yjm7Rk3EODZ/AJ63t174FW/kYK8VMIjO+8q3K5Azs+jyi+46R0VNmdkLB9uOcHzPx7lvduGKh1Oq4mkpq1ED4dBc8BmBrUW1LqTl5O3VSebgh1WsJrAWguWWrDUOSsSW3Octy01zunhljrnPk2STiY3nicTIa+Tt41nbD/H7YaL18nLyft6b+e1xtDwLi7Xm1BpRaOe+xPF91rjdPeT8j+7jLIS7vr0R9JzAvD2qeU/N/XkyqSWT00MSeqPBcg9ulMkNe1MV1yAJThM6TCapNOo+NuVPfi/T/ewOb2EUQlBSofUKiKpaStaD5j2atue0+Fwtv78PtlpuH0yMbrQ7frys7dbap3J1YVIqoZkR1czgkrbHIre3IekU6PSqZB0alBJSGrp5LWq8X2VBOrzXJ+1n+rkdn53X0Ib4ol0joRKdsjYqyxgdyA7ZLDLp69lGZWHBrVRi+ShcYkPI5chfhQtdyqpUTAEu93Bgp9+YvnmemQMXD+6luevuvWC42YuJKrXUI4DZamHYELbxiqcZrNYCS3OInf4KKVDaZYpyeG8E53Bcz8cYcUDY1Cp3O8NQyQ1rkylOt2CQhs3X9oszuTozETHUn3G7RowOx/3rLJgK6hE9otEtthxWBzINdbGiYTD8bv7MjhkZLvzwsn7LWUcGY7/NWeXp8/9+6ZmHS/p1WgCDGgCDagDPdD4G1B7a1EZtag8tag8NKg8NEiaxomT7JCxlZmw5tZgLahFtthPvza77EymTt2WZWeSdioZUzuTtt8neo2TwN8nd6rGSV9rk8JzJZenXpNYpbtVHKc67hT60a1LS2Hul9uoqAwgJrKMpTMn0zM46qLOqff2pdJbTX1GehtFKZzL8W17MdgsBI8crnQozXJqmvcNb25hxb48rh0YqXRILSaSmq5KowNNAHgGNLmrGmiLCjWyLDu/9TZKehzg4GTC4GiUDJV9erTJymd+1ySgDTU2JBKoJJAk5Hob9hoL9nIztrJ6bKUmLAdKsFeYwNH4HCovLeGPDkdSS9hK6ilfkY7lRBWy2TnoW+2jQzJoTiYujZMVSe18PofFATbb71qNHA0JXcO14xz37e04WFPCGatGhYTE7h3fk/73XeiNXhiMXuiNRgxGLzQ6PSqNBrVGg1qrdV5rzrjWap3XOi1anR61VotGp0dz8lqt06LR6jp1q1hHJ4SV9fXc98V/2XLEiM4Aj17rwb0j7m6z89eEeUN2fpudr7U2bNjASy+9xK5du8jPz+fbb7/l2muvbXj8m2++4a233mLXrl2UlpayZ88eBgwYoFi8LZG9cSvBKjW9xrnPuoBDYwOY1DuUl1YdY3JyGHqNWumQWkQkNUKHkSTp9Idscw6QZST92f9Q1sJa1AEG7GUmDL0C0PgZmh2D7JBx1Ntw1Fpx1FoxZ1ZRtSqTgpd2IGlV2KssqL20eI+LQhfpjTbCiNqrfWd+nZXsnZncnaPl65zXZ7aYOWRkB4275mwOLom8hSpNGWZLPea6GuqrKynPz8NUW4PdZsVus2G3WnGcvN0aKvXJxOhkctSQKGm0DUmSqlHipDlju/M6uk8/eowc06Y/44uhxAShpVs38c8fs7FYPBjRp5w3p1+Pn4dXmz6HPTIEj5TMNj1na9TW1tK/f39uv/12rr/++nM+Pnr0aG644QbuvrvtkrqOYNm7l5zgbvTzbdvfXXv76xU9uPzfG/jf3jxmDHGdSsjNIZIawWU5zHZUurOTmsJX94BNxiM5qEUJDTjH7KiNWtRG51gEXbQ3SCCb7Mg2B5JWhffYSFSerRur0BpnJ3vt881o0Njmd1nIsozDbnMmOjYbDpsNm8WCzWrBZrFgP3ndcN9iwWqx4LDbsVut2G1WHDbb6WTp1OWMpMl+8nGLyeTc12qlsriQ7EP7XSKpsdTXUVNeTlVuJgDrU4rJUWVjtTuw2WWsdgfWhuvTt212B1aHjM3uwOaQmT4oilHdmzfo8lhxAfd8tooTeUH4+dXx1pxkLkvo2y6vTxcbi+/mFCw2CzqNciUbJk+ezOTJk8/7+C233AJAZmZmB0XUdvzSj5A9YLTSYbRYYqg3E3uF8PaG49wwOMqtWmAvKqlZtGgRf//733nooYdYvHgxVquVxx9/nB9++IHjx4/j6+vLxIkTef7554mIuPBiXl9//TULFiwgPT2dhIQEnn32Wa677rpG+yxZsoSXXnqJ/Px8+vTpw+LFixk7duzFvATBhclmO5Lh7A94Q6I/piNlGHo23XXWFEmjwmece30T6QiSJDW0rnSkLV99xr7VPzR7f4fDjs1sxnryYjObsFrMWE1mbJZT203OfSzmk/uanPtazFhNptPbLWZsJhMWs4m6igqsZlPD89hR8cneEmoOmlBJzoJlzovUcFujltCopDNuq0gtrEYlSU0mNTa7nUe+/45vttqRJE9mXWJh4ZW3oVa1X9O/b0IvPCw/k31iPwkJQ9rtebqq4rQT+NWWUzn03FWdXd3dY+O5celW1h0r5rKeIUqH02ytTmp27NjB0qVL6devX8O2uro6du/ezYIFC+jfvz/l5eXMnTuXadOmsXPnzvOea8uWLdx444384x//4LrrruPbb79lxowZbNq0ieHDnQOsPv/8c+bOncuSJUsYPXo0b731FpMnT+bw4cPExLjWmhrCxZPtMrLVgeoc3U8qg/PPti2SGsG16D09qa+u5ue3XmlISJzJyqmk5Iz7ZlOzu8lUag1agx6tTo9Gr0erNzivT9738PHFR69Ho9OjMxjw9PXD6OeP0T8Ao58/Gm8//mTwQKtWoW7BjJDpb2xucp+fjx3kL1/toro6iIToUt6edTXxAaHNfo7WCu81mAog/8gukdS0gwM/byQc6H/lJUqH0irD4gLoH+3HWxvSO39SU1NTw+zZs3n77bdZuHBhw3ZfX19Wr17daN9XX32VYcOGkZWVdd7kY/HixVx++eU8+uijADz66KOsX7+exYsX89lnnwHw8ssvc+edd3LXXXc1HLNq1SreeOMNFi1a1JqXIbgw2ez8sDo1pka2ObAW12MrqKX+aBmaUE9UnqL3tLMJ656EX1g4xScy0Bo80Or1GLy80QYEnUxGnBeNTo/WYGiUpJxvu0anQ61R7m/lfGNySutquOfzb9l1zBe9QcVT0325bciUDosrpHsyZUBF+pEOe86upHrjJvCPoFfshXspXJUkSdx7STz3f7Kb/TkV9IvyUzqkZmnVf/oDDzzAlClTmDhxYqOk5lwqKyuRJAk/P7/z7rNlyxb+/Oc/N9p2xRVXsHjxYgAsFgu7du1i/vz5jfaZNGkSmzef/5uQ2WzGbDY33K+qqrpgrILrcFicM49qNuVRtSYLW3F9w5RwtZ8ev2kJbtXPKzRPRFIvbn/5DaXDaDPn+xN9ddOv/OfnQmxWb8b2q+aN62/AS9+y8WEXS20wUOWvxeKGY1VcXV11LaFHdlM0YrzSoVyUK/qEERPgyWu/prF0jnu05rU4qVm+fDm7d+9mx44dTe5rMpmYP38+N910Ez4+5y/hXVBQQGho4+bW0NBQCgoKACgpKcFut19wn3NZtGgRTz/9dJNxCq5HZdCgCfYAQB/rg9eIcLThRrShRlQeooVGcB/yGXUJDhXmcM+nP5NbGEqAv4lXbxzM6FjlKvrWhvmiyilS7Pk7q1+ffpkYcx197r1V6VAuilol8ZdJSTy0fC/rjhUxrofrd0O16NMhOzubhx56iJ9//hmD4cLfKqxWKzNnzsThcLBkyZImz/37b92yfHZJ9+bsc6ZHH32UefPmNdyvqqoiOloMCnUHKoOGsL+4xzcDQTifU/PZzFYr4xf/QG6pBpXGi9vHw4KJt6JSKbz8SHQ4XnsPKxpCTU0NaWlpDfczMjLYu3cvAQEBxMTEUFZWRlZWFnl5eQAcO3YMgLCwMMLCXG/5gdyUDKJ+/JLMcVOZNrCX0uFctGn9I/hsexbPfn+EsYnBLRpTpoQW/Uft2rWLoqIiBg8ejEajQaPRsH79el555RU0Gg12u7PLwGq1MmPGDDIyMli9evUFW2nA+cf5+xaXoqKihpaZoKAg1Gr1Bfc5F71ej4+PT6OLIAhCR8muyeSb3bn0WPAzuaXO75C/zhvPk5OmKJ/QAIa4BILL7FSaKhSLYefOnQwcOJCBAwcCMG/ePAYOHMgTTzwBwIoVKxg4cCBTpjjHG82cOZOBAwfy5ptvKhbzhex6bCEmrYFL//E3pUNpE5IkMX9yL1KLalixL1fpcJrUov+qCRMmcODAAfbu3dtwGTJkCLNnz2bv3r2o1eqGhCY1NZU1a9YQGBjY5HlHjhx51gDjn3/+mVGjnOtl6HQ6Bg8efNY+q1evbthHEATB1YTHbCW++3ZmjLHyypwIMp+fQmyA6ywUGNC9NzobZKXtUSyGcePGIcvyWZcPPvgAgNtuu+2cjz/11FOKxXw+u79fR+KBzVTffDe+QW1Rh901DIj2Y2KvUP69OhWb3dH0AQpqUfeTt7c3ffs2LgRlNBoJDAykb9++2Gw2pk+fzu7du1m5ciV2u72hdSUgIACdzlngac6cOURGRjbMWnrooYe45JJLeOGFF7jmmmv43//+x5o1a9i06fT6PvPmzeOWW25hyJAhjBw5kqVLl5KVlcV99913UT8AQRCE9uLtXUHPcCPPjb1W6VDOKbL3MPKAwqN7SO57mdLhuDW7zU7p889DcDfGz71D6XDa3AOXJXDdks1sPV7GmETXScx/r01HXObk5LBixQqAs9bmWLt2LePGjQMgKyurUdPrqFGjWL58OY8//jgLFiwgISGBzz//vKFGDcCNN95IaWkpzzzzDPn5+fTt25cffviBbt26teVLEARB6DJ8uiWQrYKq48eUDsXt/fqf94gqPkH9v95A7WbrJTXHgGg/YgI8Wbk/z6WTGkmWlVjZRBlVVVX4+vpSWVkpxtcIgtDubv3xViK9Inlu7HNKh3Jev10ykMLugfzhvTVKh+K2KkvKOTLxCooSk5n25btKh9NuXlp1lI+3ZrHjsYnoNB07Jqy5n9/Kj1QTBEHoxFy9nlLtpQPptTmXVY/fjsPh2uMlXNX6BS+it5oZ/OwCpUNpV1f3i6Cy3sqmtGKlQzkvkdQIgiB0YROffofjs0YR89VWfnrkZpHYtFD6niPErvuOnMnTiUyKVTqcdtUzzJvuIV6s3JevdCjnJZIaQRCEdnJm4T1XpVKpmPLku5y4bTxx3+1h9Z9nisSmBY4s+AdVnj5MeHJe0zu7OUmSmNovgp8PF2Ky2pUO55xEUiMIgiBw5fzXybh7EjGrDvDLgzeIxKYZtiz/noS0PdjueRBPb6PS4XSIq/uHU2O2se6Ya1aiFkmNIAiCAMBVf/kP6fdPJuqXw6z611ylw3FpFpMZ0+J/khnVg0vuvlHpcDpMQrAXiSFerDvmmuNqRFIjCIIgNLj6Ty+TOaEn4ctWk31wm9LhuKw1zy8huKKQmCcec4nq0B1pUIw/e7MrlA7jnLrWb0IQBKEDuWvFjDHPvU21t5q0ef+H1W5VOhyXU5ydT8jXH5I+bAJ9LhmqdDgdbkCMHymF1dRZbEqHchaR1AiCILSjU4tauhNv3yAs100kLKuGQwfXKR2Oy9n8uLMa/qhn/65wJMroH+WHQ4YDOZVKh3IWkdQIgiAIZ/GJ7wGAqbigiT27lsMbd5KwbQ1F188hODpc6XAUkRTqhbdew9bjZUqHchaR1AiCIAhnCe7WE4D8xS8rHInrcDgcnHh6IcV+oUycf7/S4ShGo1YxNimIdSmuNwNKJDWCIAjtxB3q1JxP7NDLSI3V0zPNhM1iVjocl7DhnS+IzTmGYe7D6Ax6pcNR1LgeIezNrqCs1qJ0KI2IpEYQBKEdufoyCRfiiAkDYPMn/1I4EuXVVdeifutV0hMGMHLmFKXDUdzYxCBkGbZnuFYXlEhqBEEQ2pHVYeVI6RG+Tf2W57c/z6yVs3hy85NKh9Us0978gSqjiqo9O5UORXG/Pv1vfOsq6bXwCaVDcQnhvh6EeOs5mOtag4U1SgcgCILQWdkddr4//j3fH/8eCYluPt3IrMqkqM71xiKci0qlorh3GPqUbKVDUVRuSiaRP35J5ripTBvYS+lwXEa/KF/2i6RGEASha5g3ZB5pFWn0CuhFkn8SnlpPHt34KHk1eUqH1myaXkkE71mHzWJGo+ua40h2PfYPQrR6Lv3H35QOxaUkR/rxweYMZFl2mW5W0f0kCILQToaGDWVWz1kMCBmAp9YTgApzBT56H4Uja76gAcPR2yDz0BalQ1HE7u/XkXhgM9U3341vkL/S4biUflG+lNdZySmvVzqUBiKpEQRB6EDBHsEU1hYqHUazJQweD0D23o0KR9Lx7DY7pc8/T05wN8bPvUPpcFxO30hfAPa7UBE+kdQIgiB0oF6BvUitSMVid62psOfjHxpDpbeaqiMHlQ6lw/36n/eJKj5B4Pz5qDVqpcNxOcHeeiJ8DezPrVA6lAYiqREEQehAvQN7Y3PYSK1IVTqUZquK8kN9PEfpMDpUZUk5Xh8tJTV5FIOmjFM6HJeVHOXrUssliKRGEAShAyX5JwFwrOyYwpE0nz02Ej8X+uDqCOsXvIjBambwswuUDsWl9Yvy40BuJQ6HaxSaFEmNIAhCB8qtzgUgzDNM4UiapzQrFc89KdjVrjG7pSOk7zlC7LrvyJk8ncikWKXDcWnJkb5Um2ycKPv/9u47PqoqbeD4b0pmkkx6IR0SegktdJAOQRYBBV1BBCygqCCubRd7x9cOiLKwiA0FCwsoCAbpvQZCDyUkIQkhvU8yM/f9gyVulpI2k5mE5/v5zOf9MHPvc5/zHtj7eO655xTZOxVAihohhKhTO1J2oNfoiQqIsncqlUpPPcuRCWPQFhrxff8de6dTZ068/CZ5rh4MfvVpe6fi8NqXTxbOsW8i/yFFjRBC1KFdKbvoEtAFZ62zvVO5qeycSxyZeA+GIjNNvvySyN4j7Z1Sndi1bA3NzhzC9MgMXN0N9k7H4XkbdDT2cXWYeTVS1AghRB26WHARPxc/e6dxU6UmI1sfHYvfpWI8Pv2Q0Hbd7Z1SncjPzsP40XskhLai39R77Z1OvdHegVYWlqJGCCHq0B1N72B9wnpySnLsncoNrX1+As0PZ2J57Sla9xpu73TqzMbp/8CzMIcW//c2arXcHquqfYgnxy7moij2nywsvSaEEHVIq9ZiNBvJMjrW7sZX/T7/H7Rae4xLDw4jauyj9k6nzmxeuIyWBzZx+YEnaN6lnb3TqVcCPZwpLDVTUmaxdypS1AghRF05l3uOeYfm8WDkgzT1bGrvdK6x/7cvCZq/ioT+LRj4/Mf2TqfOJJ86j2Hee8S36c6QZ6bYO516p3kjNwD2Jti/UJeiRggh6sjGxI1oVBqmd5pu71SukRC3E2a9x8XmngyZu9xhNii0NVOZibjHn8Koc6bv5x/JY6caaBfsQaCHM7vOZto7FSlqhBCirrT0bkmppZQLeRfsnUoFxqICEh+bRqG7lh6Lf8RJ72LvlOrMuhf/j8YX49G/8gbegb72TofPPvuMiIgInJ2d6dKlC9u2Of6eWyqVisgQT46l2H+ysBQ1QghRR3oG9cTgZGBD4gZ7p1KByVSKk9FMqasOV7dbZyfq2PXbCf/lO85Gj6Xb6CH2Tofly5fz1FNP8eKLL3Lo0CH69u3L8OHDSUxMtHdqlWrs40pqbom905CiRggh6opOo6NPcB92peyydyoVGDx8MHzwBo2SC9kw816HeIvF1vIyc8h7aRYXG4Vz+3sv2TsdAD766CMefvhhpkyZQps2bfjkk08ICwvj888/t3dqlfJw0ZJXXGbvNKSoEUKIumSymOr8mrNnz6Zbt264u7vTqFEj7rzzTk6dqrj3VIf+Y8mYcTdNt57jj0+eq/Mc69qmJ/6OW3E+LeZ+hM5Zb+90KC0t5cCBA0RHR1f4Pjo6mp07d9opq6pz0qgxO8D+T1LUCCFEHcooyeBQ+iGKyupur5wtW7bwxBNPsHv3bmJiYjCZTERHR1NYWFjhuMHT3uRcBz90P/9eZ7nZw8bPvqVl7FYyp8ykacdW9k4HgIyMDMxmMwEBARW+DwgIIC0tzU5Z1T9S1AghRB2a3HYyAK/ufLXOrrlu3ToeeOAB2rVrR8eOHVmyZAmJiYkcOHCgwnFmixmvxGzy2jW+abySnExO/bGCbR/9nZgnxnLh8A5bpm9VicfP4Pn5h5yO7M3gJyfbO51r/O9bZ4qi3DJvolmD1t4JCCHErSQ6PJrmh5uzLmEd7/d/3y455OZeeUvFx8enwvdxm37EJ8eMy5hxwJUbauaFUyTs30R23EEsp8/hnnAZ7+wrcyc8tOAOxJW9Q5OFa+q0DdWlKAq7fttBwdtv4Kw30O+z9x3q9W0/Pz80Gs01ozLp6enXjN44IhcnDQVGk92LMClqhBCijvUN6cuZnDOczDpJa5/WdXptRVF4+umnue2224iMjKzw26VvvqIxkPbbKtZ/Nh+fpFzcihQMgOIC6aFupPVoSk7r1gR07EGzDv3Y8erjNNoYR1lpCU46x9yk88jW/Zx972NanzmIyTsQ/w8+xLuRT+Un1iGdTkeXLl2IiYnhrrvuKv8+JiaG0aNH2zGzqgnydMZospBTVIa3QWe3PKSoEUKIOjay2UiWHFvCfWvuY2bUTCa2nYhaVTejBtOnT+fIkSNs3779mt+0yekAeOw+QU4THy79pStFkR0Ii+pHi2ZRaDXX3jKajB6P5tcjHI75nq4jHrR5/tWRHHeKA6+9S/Nje/Dz8CVrxixue2Q8aicne6d2XU8//TQTJ06ka9eu9OrVi4ULF5KYmMi0adPsnVqlAj2vFLSpuSVS1AghxK2khXcL9t+/n7kH5/LB/g/YkryFt/u8TZBbkE2vO2PGDFavXs3WrVsJDQ295vfuP67FZCqjjf+1v91I2z6j2OX1Etm/rgAHKWrKSoxseP1jglctxdfZndSHZzLgyQfR6u13s62Ke++9l8zMTN544w1SU1OJjIxk7dq1NGnSxN6pVSrA40pRcym/hLZ42C2PWv2nwezZs1GpVDz11FPl361YsYJhw4bh5+eHSqUiNja20jgDBgxApVJd8xkxYkT5Ma+99to1vwcGBtYmfSGEsBu9Rs9z3Z7jX9H/IjEvkTGrx/DL2V9sskaMoihMnz6dFStWsHHjRiIiIq57nLt3AN7VKGgA1Go1Ob3a4L/3LKayUmukWyuHN+5hy5CRhK38hjP9RtB+43qGPPeowxc0Vz3++OMkJCRgNBo5cOAA/fr1s3dKVaLXXiknykz23dSyxiM1+/btY+HChXTo0KHC94WFhfTp04d77rmHqVOnVinWihUrKC398x9DZmYmHTt25J577qlwXLt27diw4c+VODUaTU3TF0IIh9AjqAcrRq/gnT3v8ML2F9ictJlXer2Cp97Tatd44okn+O6771i1ahXu7u7lk1E9PT1xcan9lghho/6K02+vsGn+CyhmM14t2tJ9VNX+99+aEk6cR/f4A4QAZfOXMHZwzzrP4Vbl4eKETqO2+6rCNSpqCgoKmDBhAosWLeKtt96q8NvEiRMBSEhIqHK8/52Bv2zZMlxdXa8parRarYzOCCEaHA+dB+/2fZcBoQN4c/eb3LXqLt7q8xa9Q3pbJf7VFWkHDBhQ4fslS5bwwAMP1Dp+ZL8x7PF4ndAFV96AyvTeAHYoaoKbhnDAxYM8v2CGSUFTp5w0apr6GziTXmDXPGr0+OmJJ55gxIgRDBlim70yFi9ezLhx4zAYDBW+j4+PJzg4mIiICMaNG8e5c+duGsdoNJKXl1fhI4QQjur2iNtZMWoFzb2a8+iGR1l3fp1V4iqKct2PNQoaALVGQ/CSf8GSD0l7dhy+2SbSk05VfqKV6fQ6su+bQuOkk6SdvPn9QVifs5OGUjs/fqp2UbNs2TIOHjzI7NmzbZEPe/fu5ejRo0yZMqXC9z169ODrr79m/fr1LFq0iLS0NHr37k1m5o23Op89ezaenp7ln7CwMJvkLIQQ1hJgCGDB0AUMbjyY9/e9X6crD9dGk3Y9adPrL7QYcOX14zPb1tolj8CObQHY989v7HL9W5lWrcJk560SqlXUJCUlMXPmTL799lucnW2zHsHixYuJjIyke/fuFb4fPnw4Y8eOpX379gwZMoQ1a64Mc3711Vc3jDVr1ixyc3PLP0lJSTbJWQghrEmtUvNct+fIMeaw+Ohie6dTLSHNOpLtoSbn4J46v7bFYiH1o0/I1xtoedfwOr/+rU6tVmGx82ao1SpqDhw4QHp6Ol26dEGr1aLVatmyZQtz585Fq9ViNptrlUxRURHLli27ZpTmegwGA+3btyc+Pv6Gx+j1ejw8PCp8hBCiPghxC2Fyu8l8efRL0grrz94/KpWKrGb+OB2v+8c/h2N20DThKCUz/06rft0rP0FYlVatsvumltUqagYPHkxcXByxsbHln65duzJhwgRiY2Nr/TbSDz/8gNFo5P7776/0WKPRyIkTJwgKsu26DkIIYS8PRT6Eq5MrCw4vsHcq1aLu0IZGifmYSur2TZikxV9jQUX7OwbV6XXFFZr6VtS4u7sTGRlZ4WMwGPD19S1fbjsrK4vY2FiOHz8OwKlTp4iNja2wn8WkSZOYNWvWNfEXL17MnXfeia+v7zW/Pfvss2zZsoXz58+zZ88e7r77bvLy8pg82fE2JBNCCGtw07kxpf0UVp5ZSUJugr3TqbKA7v3QmeDM/g2VH2wlaeeSaHFkO+f+Mg7vRtfeQ4Tt1buipipWr15N586dyxfOGzduHJ07d2bBgj//SyMxMZHU1NQK550+fZrt27fz8MMPXzducnIy48ePp1WrVowZMwadTsfu3bvrxUqLQghRU+Naj8PPxY/5sfPtnUqVte45nDINXNyzqc6uGbthFwDNxtxRZ9cUFWlU9p8orFJssXylg8rLy8PT05Pc3FyZXyOEqDdWxK/g1Z2vsvyO5bT1bWvvdKpk/bAoLAF+DP/6d5tfy2Kx8NWgewjLT6fv7s3onWRhVnt45Ov9mCwKXzzQzeqxq3r/dpx914UQQlzXqGajCPcIZ+6hufZOpcpKWjfB83Rq5QdaQczHS+iZdpysCVOloLEjTX17pVsIIUTd06q1zOg8gx0Xd7AvbZ+906kS96gueOeYyL5o27egLhw7i++Xn3KmUz/++vQkm15L3JxarcIiRY0QQojKDG0ylLa+bZlzcI5NNr20tojetwNwZtsam13DbDJz7KlnKdG50G+ebRaEFVVXUGLCoLfvSJkUNUIIUQ+oVCpmRs3k8OXDbEneYu90KhXevAuZHmqyDuy22TXWvTmHiKST6F98DU9/n8pPEDaVU1SKt6t9d0OXokYIIeqJXkG96B7YnTkH52C21G6xU1tTqVRkNvdFe+yMTeL/MvNVmi5fRHzfEXQbE22Ta4jqySkuw9PVya45SFEjhBD1xNXRmjM5Z1h73j57K1WHOrI1/hfysBiNVo1rMZtpvv4HAIZ89LpVY4uayy6UkRohhBDV0MG/A4PCBjE/dj5l5jJ7p3NTjbr3w8kMCQc2WzXuia1XJkvnvjMXV3eDVWOLmjFbFPJKTHi5yEiNEEKIapjReQYpBSm8uP1FdqbsdNjipnWv4ZRqIHn3H1aNe+6X3yl0cqbz8H5WjStqLrf4yt9BLxmpEUIIUR3NvZvzfLfnib0cy6Mxj9J3eV+KyorsndY1PA2+pIQ6UxJ7xKpxdft3kdqyE3oXvVXjippLy72yz5efmxQ1Qgghqun+tvezfux6ZkbNpLCskNuW3cbU36fy9bGvOZ973mFe+y5uFYb76RSrxUs8m0Tj9ATc+/e3WkxRe0dTclGpoHWQfVfrl6JGCCHqKZVKxZT2U1hz1xqe6foMGpWGOQfnMGrlKEatHMXlosv2ThFDVBReOWXkJSdYJd6Rn37DgopOd99ulXjCOo5ezKWpnwE3vdaueUhRI4QQ9Vxjj8ZMaDOBBUMXsG3cNl7q8RIJeQmkF6XbOzWa9BoGwNnt1nlby7R1MynBzfAKDrRKPFF7iqKw93wWHUK97J2KFDVCCNGQuDq50sa3DQA6jX3nNwC0aNGdDE8VWQd21TpWdkYu4efjUPoOtEJmwlqOp+ZxMi2fUR2D7Z2KFDVCCNHQGM1X1oXRa+w/kVaj1pAZ4QMnz9Y61oGf1qK3mIgcN8oKmQlr2XQyHYNOQ5/mfvZORYoaIYRoaErNpYBjjNQA6EJC0Gbm1TpOQUwMKX5hBLdpboWshDWYLQo/7E9maNsAdFr7lxT2z0AIIYRVXR2pcZSixiUgGI98MyazqcYxinPzaHVsFyW9ZG0aR7L5VDqJWUVM6h1u71QAKWqEEKLBuTpS4wiPnwBc3X1wLoOsrIs1jnH01Su7cDfv1t5aaQkrWLIjgQ6hnnQO87J3KoAUNUII0eCsT1iPv4s/zhpne6cCgCEoDIDM+Lgax7h89gIALe+R+TSO4lBiNtvPZPBov2aoVCp7pwNIUSOEEA2KRbGwOXkzk9tNRqPW2DsdAEJ6DwIg/URsjc7PuJxDRPwhkkbe5zA3TwE7zmTgrtcyPNJxXq+XokYIIRqQrJIsTBYTjd0b2zuVct6NGpPpraF45a/E76/+PlC7v1h+ZcG9R+63QXYN2+lL+Ry9mGuTFaY3nkynR1Mf1GrHKTSlqBFCiAbI0UY0zFPuxSs1H9P901l/Rw/2/fKvKp+rXvcLSRHtaNQiwoYZNhzpeSX8sD+JexbsJPrjrdwxbztTv95v1WucSc/nYGIOd3UOtWrc2pKiRgghGhBPvScAaYVpds6kov4Pv0zUtn2k/2MiKpMZ579/yO4Vn1V63ok9cUSknsVw1111kGX9pSgKu85mct+i3XR/5w/+/vMRNGoVn02IYly3ME5dyrfq9b7aeQFvVyeGtG1k1bi1Zd9NGoQQQliVk9qJXkG9+On0T9zb6l6HGrHRubjS/4EXKLvvaWLuH0rwq/OI8wmk/YAxNzzn1JLvCNG50v1+KWquSs8rYc4f8VzKM2LQa9CoVBxNyeX0pQLaBnnw4T0dGdDKH1+3K2+/peQUsyo2BbNFQWOFR0VnLxfw/d5EnoluhV7rGPO2rpKRGiGEaGCmtJ/CqexT7EzZae9UrstJ50z/L1aTHupGyd9e4tyR7dc9rrTESMDuP0jtPgC9q0sdZ+l4dp/LZMb3h+j73iZWH06hzGwhLbeE85mFRDX2ZvHkrqx58jbGdgktL2gA2od4Ulxm5rQVRmssFoVXVx0jwMOZB/uE1zqetclIjRBCNDDdArvh5+JH7OVY+oT0sXc612Vw86bbVz9z+O6RlD4yDdflPxLYpE2FY7b/azlBJfl4TR5vpywdw4XMQt5Ze4L1xy7RMsCNp4a05L4ejfF0carS+a0DPQA4nJRDmyCPWuXy7Z4LbD+TwdcPdcfZybFGaUCKGiGEaHBUKhW+zr5kl2TbO5Wb8m7UmJZLviFh/H2ceGA8Lj+uxdPvyqaIxqJidF/+kzMtohjZt6udM7Wfj2NOM3/TGfzc9MwZ14lRHYOr/Ujxm90JOGlURDXxrnU+W09ncFtzP/q19K91LFuQx09CCNEAeTt7k1WSZe80KhXcrAP+C+binl3Knsl3UlJ05RHJlv/7HK/CHJq//A87Z2g/2+MzmPNHPFP7NWXzcwMY3Smk2gVNqcnCV7su8NeuYbQMcK9VPoqicCwllzZBtYtjS1LUCCFEA+Tt7E2OMcfeaVRJi6hBaD98hUYX8tn48EhyU9Lw+fe3nOw6iDbdG862CGVmC6UmS5WOLSo1MevfR+jV1Jfnh7Wq8aOeHWcyuJxvZEKPJjU6/78lZhWRmltCz6a+tY5lK/L4SQghGiAfZx/is+PtnUaVdRo8jh0vX6LxawtIGTQQlVZPt1eet3daNZZXUsaXOxLILDDiqteSmlPMtvgMyswWZo/pwIgOQTc8t9Bo4snvD5GRX8o3D/Wo1Rts646mEeFnsMroSmxSDgBdw31qHctWpKgRQogGyEvvVS8eP/23PvfO5OvTcXRbuoPj/fswsUWYvVOqtvT8En49nMqnm85QXGqmia8rBUYTgR7O3NU5hJTcYp747iAbT4bywl9aV3hLyWgys+lkOu+sPUlGgZHPJkQR7meocS6bT6Wz7lga47qHWeXV/qujTAad400QvkqKGiGEaIB8nH3INeZiUSyoVfVjpkH25WRC1+4m0UfLbdOesHc6VXYiNY9fDqew5fRljqXkoVLB3VGhPDusFQEeFTcVVRSFnw9e5NVVR1kZe5EOoZ4Ee7mQV1xGbGIO+UYTvZv58uWD3Wjq71ajfNLzS3j9l+OsOZJK93AfHuxtnZWYy8wKapXjrVb936SoEUKIBsjb2RuzYibPmIeXs5e906nUmcNbSZ4xA/diC8UaD9ImPwT//CcR3TraO7WbSs4u4q7PduCq09KvhR9T+zalbwu/CiMw/02lUnF3l1AGtvJn3bE09idkk5pbjJeLjil9mzK8fSAtGrnVuHBIySnmngW7KCkz88m9nRjdqfpvS91IWm4xAR7OVlnAz1akqBFCiAbIW3/l9d0sY5bDFzXblr6P4f++QOWpw/urz2lkaMqpyQ/h9PCD5L/3CR1u72fvFG9o9tqTeDg7sfHZAbjpq35L9XXTM6FHE6tM4L2qzGxh2rcHAPhlxm0Ee1l3wcKLOSVWj2lt9WNMUgghRLX4OF+ZzOnIa9WUlhbzy7P34vfmF6RFBtHllw0079ifJs3DiPp5Gel+YViemc6O71bbO9Xr+v1YGmviUpn1l9bVKmhs5V/bznMsJY/PJkTZpPhIzi4iyNO58gPtSIoaIYRogLydr4zUOGpRk34xnj/GDqDpmiMkTxrM7d9uwM3rzwXd/AJ96b/yO5KbRuLx5izWzfnKjtleKy23hOd/PsLQtgHc2SnE3umQV1LGgi1nub9HYzqGeVk9vsWicDwlj9aBjrtGDUhRI4QQDZKn3hO1Sk220fGKmkObf+TMXXfim1JA2UcvMPSFT1Grr70dGTwM3P7zVyR27kvY5//HTy9/jKIodsi4IrNF4W/LY9Fr1fzf2A4OMXF21aGLFBhNPDaguU3in8soIN9oonPj2q9KbEtS1AghRAOkVqnx0ns51EiNxWLht0+eRvPEKxR5u9Lk5x/pNHziTc/R6pwY/s18koeMpt2PC/nu8VcoNZnrKOPrW7DlLLvPZ/LxvZ3wMejsmstVvx5JpX9LfwJt9HhobVwaeq2aDqGeNolvLVLUCCFEA+VIRU1Bfha/PDyM8AW/cXFAG/qt2kxAeNsqnavWaIie9w7p4x4matNPrH9nvo2zvbGDidl8FHOax/o3o3czP7vl8b9OpuUT1djLJrFLysx8vSuBe7qG4u5ctU007aVWRc3s2bNRqVQ89dRT5d+tWLGCYcOG4efnh0qlIjY2ttI4X375JSqV6ppPSUlJheM+++wzIiIicHZ2pkuXLmzbtq026QshRIPmpfci15hr7zQ4e3wHe0YNInxfMpnPTmD4/BXonKu3qJxKpaL/a89ypnlnfFZ+z+qxD7I2egymMpONsr7W0Yu5PPzlPjqGevK3oS3r7LpVUWA04elqm1GjzzadIbe4jCm3NbVJfGuqcVGzb98+Fi5cSIcOHSp8X1hYSJ8+fXj33XerFc/Dw4PU1NQKH2fnP4fRli9fzlNPPcWLL77IoUOH6Nu3L8OHDycxMbGmTRBCiAbNxcmFYlOxXXPYuvwjsu+bgq7UgmHJPG6b8lKt4vlOGI+TqZTGJ/YTkXiCDe8vvOGx52JPEvPRYnZ8u5q4jXtIiU+ktMRY7WsqisKP+5O4Z8Euwnxc+eKBbjhpHOtBh0alwmyu2r5S1XEsJZfPNp/l8QHNa7W6cV2p0TtoBQUFTJgwgUWLFvHWW29V+G3ixCvPRxMSEqoVU6VSERgYeMPfP/roIx5++GGmTJkCwCeffML69ev5/PPPmT17dvUaIIQQtwBXrSsFpQV2uXZZmZHfXpxMi9WHudAxgNs+W46bb0Ct4/YePxLGjwRg1cQZNF76Ty6MjqZJu4oTZHf+8Bv6N18gtOzPEf/c/3zy9QYKXD0pcfekLLwZje+8gw7Rt6HRVlz+/8qu1Hm8teY4u89lMaZzCG/f1R4XB9wmwMNFS26xdUetEjIKeXDJPloGuPPEQNtMQLa2GhU1TzzxBCNGjGDIkCHXFDU1VVBQQJMmTTCbzXTq1Ik333yTzp07A1BaWsqBAwf4xz8qbkEfHR3Nzp07rXJ9IYRoaNx17qQVptX5ddNTzrD/sQk0O51H8oQBRL/wKWqN9QuBwfPe5nj/QcR+8k+aLHq//Pu17y8i7ItPSIyIpNfiTykpKCYzKZW8lEsUpV3CeDkDc0YGqqxMGu3bivPWX9nt6kVG514U9+7P+eCWWCwKG06kcz6jkMY+rnz9UHf6tfS/STb2FezlQnJ2kdXi7TiTwd+Wx+LmrOWrh7qj0zrWyNSNVLuoWbZsGQcPHmTfvn1WS6J169Z8+eWXtG/fnry8PObMmUOfPn04fPgwLVq0ICMjA7PZTEBAxSo/ICCAtLQb/4M1Go0YjX8ONebl5VktZyGEcHTFZcXEZcTV6TUPbf6Ror+/hp8Jyj6cxdC/TLLZtdy8PEjrezuhW9aQl5WLq4cbq598mTYb/83ZHkO5fdGHaHVXJrYGt2h83RimMhOH120lY+UaWu5YCzt+4593v02pmxe9m/nyysi29Gnm5/A39fYhnuw8m1mrGIqisP9CNt/susDqwyn0ae7Lx3/thL/79bd8cETVKmqSkpKYOXMmv//+e4X5LrXVs2dPevbsWf7nPn36EBUVxbx585g7d2759/+7FoCiKDddH2D27Nm8/vrrVstTCCHqk98Sfquza1ksFn6f8wwhi9ZREmag+T+/rvLbTbURNeNhcv74N1vemYdy6gSt4g+R9NeHGfH6M1VaP0brpKXLyEF0GTmIlJdcyf3pJzY8PwSNh4fNc7emfi39WbonkfhL+bQIqP4CeW/8cpzVh1PIKDAS7uvKm6PbMaFHE9QOvM/T9VSr9Dxw4ADp6el06dIFrVaLVqtly5YtzJ07F61Wi9lsnbUD1Go13bp1Iz4+HgA/Pz80Gs01ozLp6enXjN78t1mzZpGbm1v+SUpKskp+QghRHzzQ7gEASkwlNz+wlgryMvn1oWE0+ec6kge1oe+qrXVS0ACEtorgXOuuNP91KcEJx8l9+V2i33i2Rgvi+T85A7RacleutH6iNjaglT+BHs58vvlstc89ejGXJTvP0ybInaVTerDxmQFM7BVe7woaqGZRM3jwYOLi4oiNjS3/dO3alQkTJhAbG4vGSs9MFUUhNjaWoKAgAHQ6HV26dCEmJqbCcTExMfTu3fuGcfR6PR4eHhU+QghxqxjTYgwAO1J22OwaZ+K2sXfUIBrvTybzufsZ/ukKdM6uNrve9bR54TlO3zaCkJWr6D1hVI3jODVqhPuQIeSs+LcVs6sbeq2Gaf2bsjL2IoeTcqp8XkmZmb8tj6VtkAeLJ3ejT3O/elnMXFWtx0/u7u5ERkZW+M5gMODr61v+fVZWFomJiaSkpABw6tQpAAIDA8vfbpo0aRIhISHlby29/vrr9OzZkxYtWpCXl8fcuXOJjY1l/vw/F1h6+umnmThxIl27dqVXr14sXLiQxMREpk2bVsOmCyFEwxbhGUEL7xbEXIhhcOPBVo+/7bsPcP2/xWjdnXD7aj6dugyy+jWqokW3SFp0+8AqsTyGD+fizJmUJiWhCwuzSsy6Mr5HY1bGpjD16/2snn5bpasLK4rC22tOcCGriF9n3Obw84aqwuotWL16NZ07d2bEiBEAjBs3js6dO7NgwYLyYxITE0lNTS3/c05ODo888ght2rQhOjqaixcvsnXrVrp3715+zL333ssnn3zCG2+8QadOndi6dStr166lSRPrbdsuhBANzdAmQ9mctBmjufrrs9xIWWkJvz7zV/zeWMyltoFE/bKB5nYqaKzN0KsnqFQU7b32ZRjFYv11YKxJr9WwcFIXtGoV4xbu4sCFrBsem19SxvM/HeGb3Rd4+Y62tKzBPBxHpFIcYXewOpKXl4enpye5ubnyKEoIcUs4l3OO0atGM2/QPAaEDah1vPTk0xycdj+hZ/NJmTiIIf+Yd93NKOuzcyNH4tI5Cs87R5O/4Q+K9u2jNDERS24uajc33AcPxnfKw+hbtLB3qtd1PqOQvy2P5XByDuO6NebuLiE09jHg6eLExZxitsdfZs4f8RQYTbx9Z3vGdgm1d8qVqur9W4oaIYRo4O5ceSdtfdvyTt93ahUndsP3FM96C5UCzm+/QKdhE6yUoWNJfupv5K9bB4DGzw+3Pn3QRUSg8fHGlH6ZnJ9+wpSWhu+jj+L/5AxUNliDp7bMFoXF28+xaNt5LudfO0o3ulMwf7+9NcFeLnbIrvqqev+u0eJ7Qggh6o+h4UNZenwppeZSdJrq7w9ksVjY8MFTBH0ZQ364Ox0XfE1A49Y2yNQx+EyaiK5JE9wG9MelY0dU/zMS5ffIVDKXfMnlOXMoOXqU0E/noXZxrOJAo1bxSL9mPHxbU+Iu5nI530h2USlBns60C/Z0mN3FrU1GaoQQooGLz45nzOoxzB88n36h/ap1bl72JbZNH0/TA6mcGx7J0He/Qqev27ebHFXhrl0kTXsMr7FjCXzlZXun06BV9f7dsB6ECiGEuEZzr+aEe4Tze8Lv1Trv5MENHBw1lKC4VDJefJARH/8oBc1/MfTqRaPnnyP7u+8o2LLF3ukI5PGTEEI0eCqViujwaL4/+T1l5jKcNE6VnrPhi9fx/XgZKh89Pkv/RVT7PnWQaf3jfd99FGzazKXZ72Lo169Gi/7Z2pasfBYkpZNcUkpmmQmtSkU3TwO3ebszLtAHFwfbcbw2Gk5LhBBC3FB0k2jyS/PZk7bnpseVFBewavooQt5bxqWoxnT7ZSPhUtDckEqlwvfhhyhNSKA4Ntbe6VRwubSMiUfOce/hs+SazAz08WBaWCPuDfQhs9TEy/HJ9Nt7krWXc2goM1GkqBFCiFtAS++WuDm58diGx9iavPW6xySfO8yWu/rTdGM8aY/ewbAv1+Hq4VPHmV7r888/p0OHDuUrw/fq1Yvffqu7fa0q49qjB9qgIPLWrLV3KuV25xQweN8pDuUVsTgynDVRLXijRQhPNgngxWbBrIxqwZburWnuquehowm8cTbF3ilbhRQ1QghxC1CpVDzT9RkAntr0FJuTNlf4fe+vi7l4z3g8soyoP3+bgX9732EepYSGhvLuu++yf/9+9u/fz6BBgxg9ejTHjh2zd2oAqNRqXDt3wvifFfTtLd1YxgNx52nqouePbq0Y4e913b5s5urMdx2a8lqzYD5PuszPaTderK++kKJGCCFuEXe3vJuD9x+kX2g/ntz4JHMPzqXUVMraN6ZieO4DckM8ablyNe36j7F3qhWMHDmSv/zlL7Rs2ZKWLVvy9ttv4+bmxu7du+2dWjmnJk0ovXDB3mkA8EJ8MhqVisWREQTobz5/SqVS8WiYP38N9ObZU0nE5RfVUZa2IUWNEELcQpw0TnzY/0OejHqSL+IWs/SeKCK+205WpyYM+mETvsFN7Z3iTZnNZpYtW0ZhYSG9evWydzrl9OHhmNLTsRQW2jWPA7mF/Ho5l7dahOCrq9q7QCqViv9rGUYLgzMPHj1PZqnJxlnajhQ1Qghxi9GoNUxpP4Wvhn9FgJMPis4Jv0MXyFmyxN6p3VBcXBxubm7o9XqmTZvGv//9b9q2bWvvtMrp/rMPYWliol3zWJ6WRbDeiVGNvKp1notGzReRERSZLUw7noDJUj8nDktRI4QQt6iOjTrxlx+20mzlKuDPG7MjatWqFbGxsezevZvHHnuMyZMnc/z4cXunVU7jc2VCtTk3z245mBWF1ek5jA3wRlOD+VChzjoWtgtnZ04BnyZeskGGtidFjRBC3OKKDuwHtRpDv/72TuWGdDodzZs3p2vXrsyePZuOHTsyZ84ce6f1p/Iiwn4jHGeLjOSYzPT3qfmO27d5uzM11J85F9Lr5WMoKWqEEOIWV3bhAk6BgWjcDPZOpcoURcFovHajRru5WtTUYL0Xa60Rc7ygGIC2brXbh2pG4wAUFL5NybRGWnVKVhQWQohbnMbHF1N2NorZ7JA7Tr/wwgsMHz6csLAw8vPzWbZsGZs3b2bdf3bSdij/U6BYSkoo2LaNwq3bUOl0OAUFojYYKNi+g5LjxzHn5KAYjehbtsRtQH+87rwTXXh4jS6dWFKKt1aDj1Ptbu2+Oi13B/jwxcXLPNbYH526/ox/SFEjhBC3OOc2rVGKizGeOYtzq5b2Tucaly5dYuLEiaSmpuLp6UmHDh1Yt24dQ4cOtXdq/+XaOSxF+/Zx8elnMF2+jK5ZM0qB42a42CgIZ2MJLbv1JLJtS9ROTpQcOUL298vIXPwF/k88gd+0R6udgaKA2kprC00J8+Pb1Ex+Sc9hbKD9F2CsKilqhBDiFucSFYXa1ZX8PzY4ZFGzePFie6dQKaX0P4/CtFduq1lLl3Lpndm4RkWR9/KrfJyex+9+wZTo9RXOC89Mp7OxEKcOPVG360Zidi5xoa14fc8h7uvRuVo5eDtpyDWZKDFbcK7lfk6tDS7093ZnYfJlxgR4O8xCjJWpP2NKQgghbEKt1+M+dAi5P/2MUlZm73TqJVNaGgBOgYHk/Hsll958C8/77mPFnfdyp8qDvQZPpuZdYqWnmjNdm3MkMpSFFNCupJCTGj2HnJzZ6+KOyd+f1iozL5WoOVtUUq0cOri7YlLgeGGxVdr0SJg/h/OL2Ztr37V3qkNGaoQQQuDz0EPkrlpN7qpVeN19d41iFB04gDYgEF1oiJWzc3xlaVdegc5b+xuX581DNXw4M8LbscMnhPtTzvPGmNtxdflzAq+buxujBt7GqOvEKjSZGbL/FM+dSmZF5+ZVzqGNmzNOKhWH84uJ8qj9pO+BPu40d9WzMPkyPbzcah2vLshIjRBCCJxbtcJj1Eguvft/lCYnV/k8RVEo3LOXCxMncWHC/WQvXWrDLB2X2sUZgMuffMLlNu14oFc0h739WUwBH0y4q0JBUxmDVsMLTYPZmVNAfGHVR2v0ajVdPFz596Vsq7xRpVapmBLqz2+Xc0ksdqA3zW5CihohhBAABL78MhovLy4+ORNzTk6lx5syMkiaNo3EyZMp2rcPAO/x42ycpWPyGD6clnt247F4MU+Nm0Kp1onVrUMYMfC2GsUb6uuBh1bNNymZXCg2cqKgmHyTudLzpjcJYG9uIduyC2p03f91T6A3Bo2a71Lrx2aXUtQIIYQAQOPuTujcOZSlpJAwbjwlp05f9zhFUcjfuIlzo++k5OgxQubNxXP0aJzbt0fXuHEdZ+04NJ6ePHs+nQJnF37o3Jy2TWu+QrOzRs3kYL8rj352n2DgvlO02BZHq21xPBh3npM3mDfT1cOVZi563j+fZpXRGoNGw5gAb75PzawXWyfInBohhBDlnNu2JXzZ9yTPeJLzY8bgHj0UQ/fuaAMDseTnYzx3jrzffqPsQiKG/v0IfvtttH5+ZH/z7S1d0ADsORjHppBw3i/JpHlE7beceLFZMEN9PSgwW3DTqLloLCOxuJTvUjMZtPcUz0YE8rcmASjAj2nZzEu8xJmiK4+JzhYbSTGWEeKsq3UeE4N9+Solk5jMXIb7e9U6ni1JUSOEEKICXXg44T/9SM7y5eSu/oW032PAfOXRh8bLC7eBA/F87TVce/Ysf9VXsZihlq8R13f/On6GIFcPxo+y3nYT3a8zQfexxv58cuES751PI/bSZbZ88gHJG39HlZtN8/YdeObd9+jZvbtVChqASHdXojxcWZycIUWNEEKI+ket1+MzaRI+kyahlJZiys5B4+6G2tX1usdrvLwwZ9aPeRe2kJ+Xz3r/EB7LvYRWa9tbq06t5vmIILy0Gl54cDKmhLPM+2IJ0a2a8+233/LsmNFXNvt0v35f1cRDIX5MP5FIckkpoVYqlmzh1i6rhRBCVEql0+EU0OiGBQ1c2eG79MKFOszKsazfe5BSnY6xXSLr7JoT/dzJ2RzD0jkf8/iI22nevDmvvfYaERERfP7551a91jA/T5zVKn5Mc+zCVYoaIYQQtaZv3oKyixcxZWfbOxW72JCWRXhmOi2bRdTZNU0mE2azGWdn5wrfu7i4sH37dqtey12r4d5AHxYlX6bIbLFqbGuSokYIIUStGXr3AkWhYNNme6dS54xGI1s9fBhQVlSn13V3d6dXr168+eabpKSkYDab+fbbb9mzZw+pqalWv97jjRuRazLzXarj7t4tRY0QQohacwoIwNCvL1lLlqBYHPe/5G3hlV82kOdi4N72rer82t988w2KohASEoJer2fu3Lncd999aGyw23oTFz13NfLm88R0Sh20j6WoEUIIYRV+06ZhjI+nYNMme6dSZ37ZsY+vfEP4W/oFOke2qfPrN2vWjC1btlBQUEBSUhJ79+6lrKyMiAjbPAab3qQRF41lrE7PsUn82pKiRgghhFW4RkXh2rUrGQv+aZWF3xzdmfOJPJNjpG/SOf5272i75mIwGAgKCiI7O5v169czerRt8mltcKGXl4Ef0xxz7pQUNUIIIazG78kZlMTFkf3dd/ZOxWbyc/N5Y9kqhpxKwbW0lM9u74vaxq9x38j69etZt24d58+fJyYmhoEDB9KqVSsefPBBm11zbIAP27LzSTc63o7usk6NEEIIqzF07473ffeR/v4HGHr3Rm+jxyDXo5jNmDIzMV1Kp+xiMpfnzMW1a1e0QYEoxlIUoxFUKlTOetR65yv/19kZtcGAxssbfdMInEJuvMO40Wjkrs0HOKFoMPkGMyHjIs9H98PH16fO2vi/cnNzmTVrFsnJyfj4+DB27FjefvttnJycbHbNEf6ezDqdzOrLOUwJ9bfZdWpCpdwKY4T/kZeXh6enJ7m5uXh4eNg7HSGEaJDMubmc7tETt8GDCZv/qU3iFx06ROnZcxjPnaX0fAJlqamYLl8Gk+ma4zX+fqh1elQ6HSgKFqMRpaQExWjEYjSWr5aMkxMRP/6Ac+vWFc63WCys3rCV58x68p1dUCkK25v60Sw8zOptqy8eiDvHJaOJ37q2rJPrVfX+LSM1QgghrCpj4ULQavG+969WjVu4ezfFR+LI/vZbTOnpqFxd0UdEoGvaFNfu3XAKCEAbEIg2oBFOgYFovL1RqSufZWEpKsKcnU3StMdIfellwpd9j+o/j5O27znAW2dSiA0Oo0tmMo+3bcqIjm2t2q766K4Abx49doHzRUYiXPX2TqecFDVCCCGspnDvXrK+WEKjZ5/FrV8/q8W1lJSQ+OBDoCi49e9Poy+XoAsPr1LRUhm1qytqV1eC3nqThHHjyfr6Gy71vo039h9jc0g4LZycWOJk5PZxI8r3urrVDfH1QAXsyimQokYIIUTDlLlwEc5t2+Lz4ANWjVuwcSMoCk3XrkXf1DbzdFw6dsR70kReP5fKT43zaGTw4L2SLCaMHXrddV8URUEB1LdgoWPQaAhz1nGu2GjvVCqQt5+EEEJYhSkri8KdO/H661+tMoLy37K/X4Zr1642K2iu8n/ySeKiuoFazdw+nZk0fNB1C5qcMhMTjpwjaudxlqdmUfxfWwcoisKB3EJmnLjAtGMJJDjYjd9a3DRqh9syQUZqhBBCWEVx7GGwWDD06WPVuHkxMRTt20fIvLlWjXs9GoOBf48cyANx55l4IpGXS01MDPZF/58iLc9k5udL2bx/PpUyi0IvLzdmnkzk+dNJdPEw0MRFx9asfC4ay2jirMOkKAzZd4qv2zelt7ebzfOvS84aNcUOtrJwrUrp2bNno1KpeOqpp8q/W7FiBcOGDcPPzw+VSkVsbGylcRYtWkTfvn3x9vbG29ubIUOGsHfv3grHvPbaa6hUqgqfwMDA2qQvhBDCioynTqLx8sIpJNhqMQt37SLluedxGzIY9yFDrBb3ZryctCzv1Ix7Anx4Of4ivXefYOKRc9x5MJ52248y63QyQ3w92N6jDV93aMqW7q15qWkwHlo1h/KKuN3Pk+Udm7GzZxu2dm9NZw9X7jtylg2ZeXWSf13JKDXh4+RYYyM1zmbfvn0sXLiQDh06VPi+sLCQPn36cM899zB16tQqxdq8eTPjx4+nd+/eODs789577xEdHc2xY8cI+a81A9q1a8eGDRvK/2yLvS2EEELUjDm/AI2np9Um0+b8vILU117D0KMHIR9+WKeTdPVqNR+0DuPRMH8+T0ono9REI70TrzYPZrifJyHOuvJjWxmcaWVwZmrYtWu2GLQavmnflEePJ/BA3DkWtQtnuL9XnbXDVkotFpJLSolwcZxJwlDDoqagoIAJEyawaNEi3nrrrQq/TZw4EYCEhIQqx1u6dGmFPy9atIiffvqJP/74g0mTJv2ZrFYrozNCCOGgVBoNlrJSq8XLXLwYQ8+ehM3/9MoaM3bQwuDMR60b1yqGs0bNv9pFMO14Ao8dv8CPnZzo5mmwUob2kVxShgUId7FPv9xIjR4/PfHEE4wYMYIhNhoKLCoqoqysDB+fiqs0xsfHExwcTEREBOPGjePcuXM3jWM0GsnLy6vwEUIIYRtOjcMwpV3CUmqdwkbj7o7Wz89uBY01OalVzG/ThE7urkw6co4zRSX2TqlWjuQXAdDS1dnOmVRU7aJm2bJlHDx4kNmzZ9siHwD+8Y9/EBISUqFo6tGjB19//TXr169n0aJFpKWl0bt3bzIzM28YZ/bs2Xh6epZ/wsJu3dUfhRDC1nSNm4DFQlnyRavEM+fno3ZxrJtmbThr1CxpH4GfTsu9sWc5lFdk75RqbFNWPq0NzjTS2247hpqoVlGTlJTEzJkz+fbbb3F2ts1ftPfee4/vv/+eFStWVLjG8OHDGTt2LO3bt2fIkCGsWbMGgK+++uqGsWbNmkVubm75JykpySY5CyGEoPx165K4I7WOZcrIoPTcOVw6R9U6liPxdtKyvGMzGumcGHHgNI8fv8DJwmJ7p1UtiqKwJSufAT7u9k7lGtUqag4cOEB6ejpdunRBq9Wi1WrZsmULc+fORavVYr66f0YNffDBB7zzzjv8/vvv10xA/l8Gg4H27dsTHx9/w2P0ej0eHh4VPkIIIWxD6++Pa9eu5KxcWetYhbt2AWDo2aPWsRxNsLOOVVHNebtlKHtyChiw9xQPHz1f/kjH0R3MKyKttIzBPo53T61WUTN48GDi4uKIjY0t/3Tt2pUJEyYQGxtbq7eR3n//fd58803WrVtH165dKz3eaDRy4sQJgoKCanxNIYQQ1uV591iKdu2m5NSpWsXJj9mAc/v2aP0daxdoa9Gp1TwY4seunm34qHUYxwqKid5/mvuPnOOogxc336RkEursRB8HXHenWkWNu7s7kZGRFT4GgwFfX18iIyMByMrKIjY2luPHjwNw6tQpYmNjSUtLK48zadIkZs2aVf7n9957j5deeokvvviC8PBw0tLSSEtLo6CgoPyYZ599li1btnD+/Hn27NnD3XffTV5eHpMnT67V/wOEEEJYj+eIETiFhpLx6fwaxzDn5FCwbRvuQ4daMTPHpFOruS/Il+3d2zC/TWMuFBv5y4F4vknJQFEUe6d3jTRjGavSs5kQ5OuQ20NYfZuE1atX07lzZ0aMGAHAuHHj6Ny5MwsWLCg/JjExkdTU1PI/f/bZZ5SWlnL33XcTFBRU/vnggw/Kj0lOTmb8+PG0atWKMWPGoNPp2L17N02aNLF2E4QQQtSQyskJ/5lPkh8TQ86/V9YoxuX5n6FSqfAac5d1k3NgWrWKsYE+bOjWivFBPjx3Kpm7Y89yosBx5tuYFYV/nE7CRaPmoRA/e6dzXSrFEUtBG8nLy8PT05Pc3FyZXyOEEDaiKAqpL71E3q9rCJ0/H7fbqr5tQv6GDSRPn0Gjv/8dXytvilmfbMrM46X4iySUGHkoxI9nwwPxtOPqvYqi8I/TyXyTksmX7SOI9vOs0+tX9f4tG1oKIYSwKpVKReArr+DaoztJjzxC1tdfV+lRStGhQ1x85lnco6PxmTyp0uMbsoG+Hmzq3ooXmgbzXWoWvfec5LvUTCx2GIewKApvnE3hq5RMPmgVVucFTXXISI0QQgibUMxm0j/8iKwvvsClSxc8R96Be3Q02v9ZWNVcUEjWV1+StfgL9G3b0HjxYtR6x1p+357SjGW8eTaFny9l08ndlamhftzu74mhDrYKyikz8dypZH69nMMbzUOuuxVEXajq/VuKGiGEEDaVv2ED2cuWl7+mrW/VEucWLUClpuT0KUrPnAVFwXvCBPymP4HGzfHeqnEEe3IKmH0uld25hRg0au7w9+KeQG96e7lZfdKuyaLw86VsZp9Lpchi5uPWjRlhxz2rpKi5DilqhBDCfkxZWeRv2EBJXBwlp0+DRblS4LRsifuQITgFW29374bsQrGRn9Ky+fFSFgnFpYTonbijkRdDfT3o4emGk/rPAqfIbCGnzIRercZdq0anvvmsk8ulZay5nMuCpHQSiksZ4e/Jm81DCHa271YVUtRchxQ1QgghGgpFUdifV8SPaVmsz8jlUqkJd42acBc9WWUmsspMFFsq3uL9dVpGN/IiysNAkN4JrUpFdpmJU4UlxGTmsS+3ELUKon09eTo8gPburnZqXUVS1FyHFDVCCCEaIouicLSgmJiMPC6VluHjpMXHSYOvkxZPJy1lFgv5ZgvH8otZlZ5DWmlZhfNd1Gr6ervxF39Pov088bHjm1bXI0XNdUhRI4QQQkCByUyqsQwzCu4aDUF6J4dcTO+qqt6/HasUE0IIIYTNuWk1tNDa/u2puibr1AghhBCiQZCiRgghhBANghQ1QgghhGgQpKgRQgghRIMgRY0QQgghGgQpaoQQQgjRIEhRI4QQQogGQYoaIYQQQjQIUtQIIYQQokGQokYIIYQQDYIUNUIIIYRoEKSoEUIIIUSDIEWNEEIIIRqEW2qXbkVRgCtbmAshhBCifrh63756H7+RW6qoyc/PByAsLMzOmQghhBCiuvLz8/H09Lzh7yqlsrKnAbFYLKSkpODu7o5KpbJq7Ly8PMLCwkhKSsLDw8OqsR2ZtPvWafet2GaQdku7bw2O3m5FUcjPzyc4OBi1+sYzZ26pkRq1Wk1oaKhNr+Hh4eGQfyFsTdp967gV2wzS7luNtNvx3GyE5iqZKCyEEEKIBkGKGiGEEEI0CFLUWIler+fVV19Fr9fbO5U6Je2+ddp9K7YZpN3S7ltDQ2n3LTVRWAghhBANl4zUCCGEEKJBkKJGCCGEEA2CFDVCCCGEaBCkqBFCCCFEgyBFTRVs3rwZlUp13c++ffsAyMzM5Pbbbyc4OBi9Xk9YWBjTp0+vdJ+pAQMGXBNz3LhxddGsStmy3UajkRkzZuDn54fBYGDUqFEkJyfXRbMqVZV2Hz58mPHjxxMWFoaLiwtt2rRhzpw5lcau7/1d03Y7an9Xpc0AM2fOpEuXLuj1ejp16lSl2PW9r6Fm7XbUvoaqtzsxMZGRI0diMBjw8/PjySefpLS09KaxG0J/16TdDtffiqiU0WhUUlNTK3ymTJmihIeHKxaLRVEURcnKylI+++wzZd++fUpCQoKyYcMGpVWrVsr48eNvGrt///7K1KlTK8TOycmpi2ZVypbtnjZtmhISEqLExMQoBw8eVAYOHKh07NhRMZlMddG0m6pKuxcvXqzMmDFD2bx5s3L27Fnlm2++UVxcXJR58+bdNHZ97++atttR+7sqbVYURZkxY4by6aefKhMnTlQ6duxYpdj1va8VpWbtdtS+VpSqtdtkMimRkZHKwIEDlYMHDyoxMTFKcHCwMn369JvGru/9XdN2O1p/S1FTA6WlpUqjRo2UN95446bHzZkzRwkNDb3pMf3791dmzpxpxexsx1rtzsnJUZycnJRly5aVf3fx4kVFrVYr69ats1q+1lLVdj/++OPKwIEDb3pMQ+zvytpdn/q7sja/+uqr1SpqGkpfV7Xd9amvFeX67V67dq2iVquVixcvln/3/fffK3q9XsnNzb1hrPre3zVptyP2tzx+qoHVq1eTkZHBAw88cMNjUlJSWLFiBf3796803tKlS/Hz86Ndu3Y8++yz5buJOxprtfvAgQOUlZURHR1d/l1wcDCRkZHs3LnTmilbRVXaDZCbm4uPj0+l8RpSf0Pl7a5P/V3VNldVQ+vrytSnvobrt3vXrl1ERkYSHBxc/t2wYcMwGo0cOHDgpvHqc3/XpN2O2N+31IaW1rJ48WKGDRtGWFjYNb+NHz+eVatWUVxczMiRI/nXv/5101gTJkwgIiKCwMBAjh49yqxZszh8+DAxMTG2Sr/GrNXutLQ0dDod3t7eFb4PCAggLS3N6nnX1s3afdWuXbv44YcfWLNmzU1jNZT+vqoq7a5P/V2VNldVQ+vrqqhPfQ3Xb3daWhoBAQEVjvP29kan0920DfW9v2vSbofsb7uMDzmIV199VQFu+tm3b1+Fc5KSkhS1Wq389NNP142ZmpqqnDhxQlm5cqXStm1b5bHHHqtWTvv371cA5cCBAzVuV2Xs3e6lS5cqOp3umu+HDBmiPProo7Vr3E3Yot2KoihHjx5V/P39lTfffLPaOdXX/laUqrfbHv1tqzZX5/HT/6rPfV3VdjeEf9tTp05VoqOjr7mGk5OT8v3331c5p/rW3zVpt736+2Zu6ZGa6dOnVzo7PTw8vMKflyxZgq+vL6NGjbru8YGBgQQGBtK6dWt8fX3p27cvL7/8MkFBQVXKKSoqCicnJ+Lj44mKiqrSOdVl73YHBgZSWlpKdnZ2hQo/PT2d3r17V79BVWSLdh8/fpxBgwYxdepUXnrppWrnVF/7uzrttkd/26LNtVVf+7o6GsK/7cDAQPbs2VPhu+zsbMrKyq4ZybiZ+tbfNWm3vfr7puxSStVTFotFiYiIUJ555pkqHb9161YFUM6fP1/la8TFxSmAsmXLlhpmaX3WbvfVyWXLly8v/y4lJcXhJhNW1u6jR48qjRo1Up577rkaX6M+9nd1210f+ruqf8drM1JTH/v6qupOFHbkvlaUm7f76oTZlJSU8u+WLVtW6UTh/1Xf+rsm7XbE/paipho2bNigAMrx48ev+W3NmjXKF198ocTFxSnnz59X1qxZo7Rr107p06dP+THJyclKq1atlD179iiKoihnzpxRXn/9dWXfvn3l57Ru3Vrp3LmzQ7z+eJW1260oV14DDA0NVTZs2KAcPHhQGTRokMO89nnVzdp99dHLhAkTKrwmmZ6eXn5MQ+zvmrRbURy/v2/WZkVRlPj4eOXQoUPKo48+qrRs2VI5dOiQcujQIcVoNCqK0jD7WlGq325Fcfy+VpSbt/vqq82DBw9WDh48qGzYsEEJDQ2t8GpzQ+zvmrRbURyvv6WoqYbx48crvXv3vu5vGzduVHr16qV4enoqzs7OSosWLZS///3vSnZ2dvkx58+fVwBl06ZNiqIoSmJiotKvXz/Fx8dH0el0SrNmzZQnn3xSyczMrIPWVJ21260oilJcXKxMnz5d8fHxUVxcXJQ77rhDSUxMtHFLqudm7b7Rs+wmTZqUH9MQ+7sm7VYUx+/vm7VZUa68rnu9dl8djWyIfa0o1W+3ojh+XytK5e2+cOGCMmLECMXFxUXx8fFRpk+frpSUlJT/3lD7u7rtVhTH62+VoiiKrR5tCSGEEELUFVmnRgghhBANghQ1QgghhGgQpKgRQgghRIMgRY0QQgghGgQpaoQQQgjRIEhRI4QQQogGQYoaIYQQQjQIUtQIIYQQokGQokYIIYQQDYIUNUIIIYRoEKSoEUIIIUSDIEWNEEIIIRqE/weLjEHzlItLewAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 6 unpopulated coastal tracts.  Lets plot them and their parent counties\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"We will now build the county-by-county geometries, hoping there are no islands\")\n",
    "skipList = list()  #a list of units we previously know to skip in the map\n",
    "isAlteredCounty = [False for c in range(nCounties)]\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "countyPop =       [0. for c in range(nCounties)]\n",
    "countyGeom =      [dummyPoly for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    if t not in skipList:\n",
    "        c = countyNo[t]\n",
    "        countyTractList[c].append(t)\n",
    "        countyPop[c] += tractPop[t]\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Building county geom for county\",c)\n",
    "    for t in countyTractList[c]:\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            countyGeom[c] = tractGeom[t]\n",
    "        else:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "origMAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    origMAP = origMAP.union(countyGeom[c])\n",
    "    if countyGeom[c] == dummyPoly:\n",
    "        print(\"WARNING! county\",c,\"is empty!\")\n",
    "    else:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "minTractPop = 4.5\n",
    "print(\"Here is your original county-base map before any triage; e.g eliminating coastal unpopulated tracts w pop <\",minTractPop)\n",
    "plt.show()\n",
    "if origMAP.geom_type == dummyPoly.geom_type:\n",
    "    mapExterior = origMAP.exterior\n",
    "else:\n",
    "    for i,geo in enumerate(origMAP.geoms):\n",
    "        if i == 0:\n",
    "            mapExterior = geo.exterior\n",
    "        else:\n",
    "            mapExterior = mapExterior.union(geo.exterior)\n",
    "\n",
    "for c in range(nCounties):\n",
    "    for t in countyTractList[c]:\n",
    "        if tractPop[t] < minTractPop:\n",
    "            if tractGeom[t].intersects(mapExterior):\n",
    "                isAlteredCounty[c] = True\n",
    "                skipList.append(t)\n",
    "print(\"There appear to be\",len(skipList),\"unpopulated coastal tracts.  Lets plot them and their parent counties\")\n",
    "for t in skipList:\n",
    "    plotPoly(tractGeom[t])\n",
    "    plotCenter(t,tractGeom[t],6)\n",
    "plotPoly(origMAP,0.2)\n",
    "for c in range(nCounties):\n",
    "    if isAlteredCounty[c]:\n",
    "        plotPoly(countyGeom[c])\n",
    "plt.show()\n",
    "trueMAP = origMAP  #use these terms interchangeably"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7a6c93c4-af08-47d5-90f7-dc0b4513aad5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last chance to alter the skipList, otherwise the above 6 units will be axed\n",
      "here is the proposed skipList [440, 1445, 913, 223, 808, 1258]\n",
      "here is the final skipList [440, 1445, 913, 223, 808, 1258]\n"
     ]
    }
   ],
   "source": [
    "print(\"Last chance to alter the skipList, otherwise the above\",len(skipList),\"units will be axed\")\n",
    "print(\"here is the proposed skipList\", skipList)\n",
    "#skipList = [] #...  #alter here if needed\n",
    "print(\"here is the final skipList\", skipList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2989f381-466b-4882-bf1d-cfa37a7ba8ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now preserve the original county unit lists and geoms, then rebuild as needed\n",
      "removing coastal units from countyNo 0\n",
      "removing coastal units from countyNo 2\n",
      "removing coastal units from countyNo 4\n",
      "removing coastal units from countyNo 9\n",
      "removing coastal units from countyNo 11\n",
      "removing coastal units from countyNo 12\n",
      "Here is the revised state county map after eliminating coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We appear to have some populated islands.  Separate out the main state geom in next block.\n",
      "we will scale longitudinal distance by 0.74166\n"
     ]
    }
   ],
   "source": [
    "print(\"I will now preserve the original county unit lists and geoms, then rebuild as needed\")\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "trueCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "for c in range(nCounties):\n",
    "    trueCountyTractList[c] = countyTractList[c].copy()\n",
    "    if isAlteredCounty[c]:\n",
    "        print(\"removing coastal units from countyNo\",c)\n",
    "        countyTractList[c] = list()\n",
    "        countyGeom[c] = dummyPoly\n",
    "        for t in trueCountyTractList[c]:\n",
    "            if t not in skipList:\n",
    "                countyTractList[c].append(t)\n",
    "                if countyGeom[c] == dummyPoly:\n",
    "                    countyGeom[c] = tractGeom[t]\n",
    "                else:\n",
    "                    countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            print(\"Uh oh! county\",c,\"didn't have any usable tracts\")\n",
    "MAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    plotPoly(countyGeom[c])\n",
    "    plotCenter(c,countyGeom[c])\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "origPopMAP = MAP   #origPopMAP is the map after eliminating coastal unpopulated tracts, with original islands\n",
    "print(\"Here is the revised state county map after eliminating coastal tracts\")\n",
    "plt.show()\n",
    "if MAP.geom_type == dummyPoly.geom_type:\n",
    "    print(\"Excellent!  We have no populated islands.  SKIP the below code block.\")\n",
    "else:\n",
    "    print(\"We appear to have some populated islands.  Separate out the main state geom in next block.\")\n",
    "LAT = MAP.centroid.y\n",
    "xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "print(\"we will scale longitudinal distance by\",r5(xScale))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8a562a21-4107-4a3c-a320-90442a473387",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets first identify which county each island belongs to.  Then we'll move the island to the closest county mainland\n",
      "county 9 is completely disconnected from mainland.  Move it to adjoin mainland\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#RUN THIS ONLY IF WE FOUND POPULATED ISLANDS\n",
    "nSubGeoms = len(MAP.geoms)\n",
    "subGeoms = [MAP.geoms[n] for n in range(nSubGeoms)]\n",
    "subAreas = [subGeoms[n].area for n in range(nSubGeoms)]\n",
    "mainSubGeomNo = subAreas.index(np.max(subAreas))\n",
    "mainGeom = subGeoms[mainSubGeomNo]\n",
    "nonMainGeom = dummyPoly\n",
    "for geo in subGeoms:  #the \"nonMainGeom is all pieces of the state not in the biggest piece\n",
    "    if geo != mainGeom:\n",
    "        if nonMainGeom == dummyPoly:\n",
    "            nonMainGeom = geo\n",
    "        else:\n",
    "            nonMainGeom = nonMainGeom.union(geo)\n",
    "        \n",
    "print(\"Lets first identify which county each island belongs to.  Then we'll move the island to the closest county mainland\")\n",
    "hasIsland, isIsland = [False for c in range(nCounties)], [False for c in range(nCounties)]\n",
    "isIsland =  [False for t in range(nTracts)]\n",
    "islandXshift, islandYshift = [0. for t in range(nTracts)], [0. for t in range(nTracts)]\n",
    "islandList = list()\n",
    "for c in range(nCounties):\n",
    "    if countyGeom[c].intersects(nonMainGeom):  #this county contains an island.  Find all its island tracts\n",
    "        hasIsland[c] = True\n",
    "        if countyGeom[c].disjoint(mainGeom):  #need to connect the whole county\n",
    "            print(\"county\",c,\"is completely disconnected from mainland.  Move it to adjoin mainland\")\n",
    "            isIsland[c] = True\n",
    "            countyDist = [999999. for cc in range(nCounties)]  #default, to avoid picking island counties as closest to this one\n",
    "            for cc in range(nCounties):\n",
    "                if countyGeom[cc].intersects(mainGeom):\n",
    "                    countyDist[cc] = countyGeom[c].distance(countyGeom[cc].intersection(mainGeom) )\n",
    "            closestCounty = countyDist.index(np.min(countyDist))\n",
    "            p1, p2 = nearest_points(countyGeom[closestCounty],countyGeom[c])\n",
    "            for t in countyTractList[c]:\n",
    "                islandXshift[t], islandYshift[t]  = p1.x - p2.x , p1.y - p2.y\n",
    "            for t in countyTractList[c]:\n",
    "                plotPoly(tractGeom[t])\n",
    "                plotCenter(t,tractGeom[t])\n",
    "                plt.arrow(tractCP[t].x, tractCP[t].y,islandXshift[t], islandYshift[t])\n",
    "                tractGeom[t] = translate(tractGeom[t], xoff=islandXshift[t], yoff=islandYshift[t])                   \n",
    "                tractCP[t] = tractGeom[t].centroid\n",
    "                tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                plotPoly(tractGeom[t],0.2)\n",
    "        else: #move only the county's island tracts to connect with main geom\n",
    "            mainCountyGeom = countyGeom[c].intersection(mainGeom)\n",
    "            plotPoly(mainCountyGeom)\n",
    "            plotCenter(c,mainCountyGeom)\n",
    "            for t in countyTractList[c]:\n",
    "                if tractGeom[t].intersects(nonMainGeom) and t not in skipList:\n",
    "                    if tractGeom[t].intersects(mainCountyGeom):\n",
    "                        print(\"unit\",t,\"has island pieces.  We will reduce the tract to its main state component\")\n",
    "                        plotPoly(tractGeom[t],0.2)\n",
    "                        tractGeom[t] = tractGeom[t].intersection(mainCountyGeom)\n",
    "                        tractCP[t] = tractGeom[t].centroid\n",
    "                        tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                        plotPoly(tractGeom[t])\n",
    "                        plotCenter(t,tractGeom[t])\n",
    "                    else:    \n",
    "                        isIsland[t] = True\n",
    "                        print(\"unit\",t,\"is a true island.  We will move it to adjoin the mainland in same county.\")\n",
    "                        islandList.append(t)\n",
    "                        if tractGeom[t].geom_type != dummyPoly.geom_type :  #island tract is multiple geoms.  collapse to its largest isle\n",
    "                            isleGeos = [geo for geo in tractGeom[t].geoms]\n",
    "                            isleAreas = [geo.area for geo in isleGeos]\n",
    "                            biggestIsleNo = isleAreas.index(np.max(isleAreas))\n",
    "                            tractGeom[t] = isleGeos[biggestIsleNo]\n",
    "                            tractCP[t] = tractGeom[t].centroid\n",
    "                        p1, p2 = nearest_points(mainCountyGeom, tractGeom[t])\n",
    "                        islandXshift[t], islandYshift[t]  = p1.x - p2.x , p1.y - p2.y\n",
    "                        plotPoly(tractGeom[t])\n",
    "                        plotCenter(t,tractGeom[t])\n",
    "                        plt.arrow(tractCP[t].x, tractCP[t].y,islandXshift[t], islandYshift[t])\n",
    "                        tractGeom[t] = translate(tractGeom[t], xoff=islandXshift[t], yoff=islandYshift[t])                   \n",
    "                        tractCP[t] = tractGeom[t].centroid\n",
    "                        tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                        plotPoly(tractGeom[t],0.2)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9a94118d-5863-4458-b77a-67d18cf01c2c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If you like my proposed translations, let's rebuild the MAP with the adjusted county geoms\n",
      "This would need adjustment for multi-county islands like Michigan UP\n"
     ]
    },
    {
     "data": {
      "image/png": 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oRs39jtfrJC3t34TZRhIRMQGLaQVVynatw2o3Ey7vxcLntvLTu3s454/9kWTx+yE0TFRB+quMdRDeDUK0mYxQVRvvflJVFVVRkMXMz6dFbzCgt4WROnwUmxd9TcnRLHqNHt+q11BVFa/bjdvlrK078jiddWqQ3A089pxwjKOysk4SVV/9UvfpGYQm2RuIREansyDLZnQ6M3JNsqOTLcg6MzrZXJME1dzW3j9xH0u95/DX5OnIkVeprs5k4ICXkSSJUFtPnFXllJfkExoe+F0ysd1tnH19P36Yv5OQCDNjL+nhNz87of2JpMZfZayFpLGaXV5RVexOmY3P/ZWg0FDcGKgoLqGyuIiK8koq7W5KqmXMOi/X/eclguL8v3BRa7G9egOQd7j16ykkSaoZVm/EEhzS6udXvF5cDgev3ngZUtVQRo+6H0Vx4vVW41WqUbyO4/cVJ4q3uuaxw3dfqUbxOvEq1bid5SiKb3/F6/Dto1Tj9TpQ1aZ21UknJD6W44lPzeOTkqfaJKpu8nQs0apzjmP7y2Zk2XRab8B2+wEOH3mN5OQ/ExzsK8qP7NKXgsOQf3RXp0hqAHoM7cIZs3qy+vM0FK/K+FmposVGqJdIavyRswJyd8CIP2oWQkL/oWQezmH9xoM4vTpkFIINboItMiEhZmKiw9m0pwyPAsbgpg3pF05Nrzei0xsoycnSOpRmk3U6zFYricNiOPRLPhnDt9Nv9OxWv46iuGsSHsfxxEc5IWGqSYyUExMmry+R8irVJyRKx5InZ21S5Ttnc5MnapKbY61GJmTZVNu6VOexbDreGiUbkWUzOTlfYrEkkJJ8c+35ohP6sCddorhwPzC51b+HHdXgKYnIOolVn+7HXubkrGv7NnlElOJVQJKQRSIU8ERS44+yNoKqaNpS02/2bfSbfRvgq/nQoSLpDXUKbarvnkFhiRt9aLRWYQYcc3AIlcXFWofRYhff+gof/N8VLHn5XWyRicSntu7vsCwbkGUDen3rtzadSFE8vmSpTmLkOCHxOZY01SRXirPufcWB4nXWJFMOXO6imgTKWdPy5NtHkvQM6PNfdLrj820ZjRa8ji7Y1QNt+ho7ooGTErDaTCx5excfPLCGARPjiUoMwWDW4ah046h0U13hwlHppqrCjb3UQWWJE3upE0mWCIk0Y4u2YIuyEN7VStdUG7YuQWK4eAARSY0/ylgHlgiI6ql1JICv5uOYwo2LWfb2a0goHC1W6Z3Utm8unU1IVDR5hxqecLKjMxgtzL7/Vd6//wa+fupfXPP4y9iiumkdVrPJsh5ZDkZPO8wRVQ/Jm4BLPazJtbXWfWg0Vzw0mm3LMti6JKPOzMmyTsISbMAcbMQSYiCsSxAJfSIIDjeheFXKCqopK6jmaFopu37NRvH46gKHnpPEuEtStXpJQisSSY0/yljnG/XUAYvllr/zCqXlTuIj9Ywf24dh1z+gdUgBJTIhgdwD+6iurGiT2pf2EBzWlUvv/TcLHrqfd+++mXNuvoG+oy7VOiy/YjJ0w+H9WeswNGOLtnDmFb2ZeHkvqivduJ0ezMFGjGZdk2uYPC4vOQfL+Oa/29i6JIOeI2OITvTPvynhOH+faqTz8boha5NmQ7kbUn5gMx/eMJ3MIrAFG7jwhe8ZfcdzGEKjtA4toMT28BUL7/11FSW52RpH03IxSUOYcc/f8VTr+OXTV1G8zZuLp7MLDklFZymk2l6pdSiakmSJoFAjtuggTJbmLaKrN+pI7BvBn56fSHRSCN+9/Bsuh6cNoxXag0hq/E3uDnDbNa2n+T3F5eSjfz5AXoWOIb1COPva67UOKWAl9h8EwPK3X+Xt22/EUem/b2rJfSdz1k0zqMgysebb/2gdjl+JiOqDJKnkH92jdSh+z2jWM/2mgTgq3exe7b8fFAQfkdT4m4x1oDdD18FaR1JL0ukIMfn6tQdecRdR42Zx8Nv5/PbmAyguh8bRBZbI+ARGzZxNdEp3AF7+4xxe+dNVGkfVckMn34A5XKUgI13rUPxKaHgiAOWlnWO5hLYWEmGmS3II+UcqtA5FOE0iqfE3GWshfjjoO87q45JOzwV3PUSUxckHjzzMG1dPZeGH37B06XZ2fvBvrcMLOBPmzGXG3/+P4IgojEFBVJeX8cG9t7P2S/+cdVtnBK/Tq3UYfqOqspQdm19CVSTikkdqHU7ACI8NojSvSuswhNMkkhp/oqq+lprE0VpHcpKw/mdw5XPvkRgl41H1jBvXFwkVt9OpdWgBKTSqC39+9V3O/tNfAchPP8jm777WOKqWUdwquprFboVTczmqWbX8Qrzmr1GrziQyprvWIQWM4AgzlaXi/5W/E6Of/EnxIbDnd6h6mhMZwuOY9cJXeKsqkI0m1qyZTW6+aM5tS33GTaTPuIl8eP/fKGiDmYbbg+IBvUkslNkUFaWF6MwFAPQf9DeNowksZqsBp10UrPs70VLjTzLXAxIkdtwmZ1mnxxASjs4UBMDePZkaR9Q52KJjULxeFI//jd7wusFg7DjdqR1ZZGwi/Xp+hscex679s9n66wdahxQwFK+KrBdvif5O/AT9ScZa6NIPLOFaR9IkA3uF0yVYfPJpDxFxCQDk+2HBreKR0Jsa735a/sn/Mf+O81n33bN43J23myC+2yAmnrUIyTGGYufDLF90Gy6nqAU5XV6vgk7X8eb+EppHdD/5k4x1kDJB6yiazGQ243BpHUXnkLl7BwAFh9OJ7d4xZppuCkVRUDwSR/fu4oN/XoYkyegMOnR6HTqDAZ1Bj85gRK83kPHbfirz4df3l7Np4TL6TRnBuIvuxBwUpvXLaHcWq42zLniTzb+8TqnpeVb8uJuhw1+hS3wvrUPzW4pHRRZJjd8TLTX+wl4Ihfs7bD1NfYrzC4i0iVqJ9nBsxeLkQcM0jqR5vB4nqBKF+z04Kpy4ql3YiyspzS4l/1AeR3dlcXjTIdJ+3Ud1iUrcQBuXPHQb4YmhbF24mdf/ciWL37yDihL/W+TzdMmyzMgz/0Kv5A9AZ2fbzkvYseELrcPyW4pXQSe6n/yeaKnxF5nrfbcdbCbhUykodtC3d6zWYXQKYTFxZO3eSUlOFqFR/jOLs8FoYdw1k4lJGUD3AdOafFy3/ueQe2QLq794kb3L97NvxY0kj4pjwqw7iI4f0IYRdzzJvUYR1XUxa5b/lfzKe1j53QbGn/MvDAZRp9QcXq9oqQkEIi31FxlrITQewhK1jqRJHCV5VLj0RKWIReLaw84VSwBI7DdQ40iab+wFdzUroTkmNnkYs+56h+v/+xKpE7qTsTmb9+++h08ev5KMfavaINKOyxoSzpQLPyBIuRmPaSHLF8+kOO+I1mH5Fa9LtNQEAvET9BcZ6/2qlaZwx2oAovsO1ziSwKeqau19WafTMBJthEV358KbXuLGVz5g8AWDKTxYyucPPcV7D1zK3k1fah1eu5FlmbFT76J73JtgKGDTtvM5sHM5Lmc1leX5WofXIXm9CgUZFexYmcWRXUVYQsR8Sf5OdD/5A3c1ZG+FQbO1jqTJCvZtQycphPcdr3UoAWvzdwvZ+sO3lBX43rA6Y0JzoqDgSKZe9ThnXlbFhu9fZPuPq/ju6Xf4Jf49Jl51Pb2Hz9A6xHbRvd9E8rNvwC4/xZH0TziQ9iL6kF2EGe9kxMSbtA5Pc6V5VRzZWcSRXUXkpJXicSvIskRM91CGnpOkdXjCaRJJjT84ugUUt1+11BRkHCYiyIsuKFTrUALWzx++A6pCeGxXdAYjo2dernVIHYLBGMT4i+9h7AV3sX3VO6z+8CvWf/1xp0lqAOzyUwD0H3QvO9POBUmluPoVXK6rMRqDGzzu4O5VHEh7Cgkr51z8aXuF224Obsnnh/k70ell4nqFMerC7sT2sBGdGIze2Lk/FAQKkdT4g4y1YAr1zVHjJwrzSoiOtGodRkByVFay9I2XUBUvti6xXP/8fK1D6pBknZ6hk//E+q++xhLa8Bt5QFJCQK5g+44b0FlUYoNfIrfiNtb99DSDR/2Forw0ykoOYa88jNOViUfNRjLkojOVoQ/xneLgrpX06D9J05fRmrxuhV8+SyNlUBTn/LE/BpNIYgKRqKnxBxnrIHEUyP7xR6h6vRRUKETHx2kdSkD6+aO32b9uNTq9gcsffkLrcDo0VVWx58t4HP7xt9NaJp+1BV31peiDMvBWxTNg9LmY1Stwmz5k0/bxpOf+gWLno1QpC/GoR5Hpilm6iNjQZ5l4xk48lX04cOQuyopytH4prWbf+lzsZU7GXdJDJDQBTLTUdHSKFzI3wLhbtY6kyUrTd+JRZKJS+2sdSkAKjYoG4OK//x8hkf4zfFsL1ZW+dZIUd+f6/CbLMhOnP8GmVSlEdOsLwLgp/2Tnxr5ISIRFdieya0+CrGH1Hj9q7Gts3HQx61bfxNQLvkSn8++3CkVR2fLjEboPiSY8VrQgB7LO9Zfuj/L3gLPMr+ppCnetBSC6v//E7E/ievYBoODIYW0D8QOlhQcAGHreVI0jaX+yLDNq0s2kDphc+3jQ6DkMHH05iakjG0xoAMKjE0mJfxJd8C5+XfJw+wTchg5uyaesoJrh5yZrHYrQxkRS09FlrgNZD/H+MzS64MBugvRurMmDtQ4lIHXt5fvkXXDEP1flbk8l+b7vUVhMd40j8T+9Bp2N2XMdLuMCCnP8Z02xqnIXzqrja845qz1s+DadhD7hdEkWAxcCnX+3KXYGGeug6xAwBmkdSZMVZGcTFSqDLHLm1vbdi/9h7+qVABQdFSugN6aswLd8QkSM/6yH1ZEMHXsjaze+w8E9S4nqeqPW4TSossTJ1qVHOLg5H3uZC1knERplQVVVHJW+BOe8v/jfxJRC84mkpqPLWAf9LtY6imYpLLLTI9k/VhL3N4c2b6i9r+vk89I0RUVhHrJBxWwVn9CbKvPQJvYf9k0P4Km2obeolJWuBzpmUnN4RyHL3t2NJEn0Hh1LbHcbVeUuKoqqQZLQG2V6j4olLMZ/PhgKLSeSmo6sNBPKMv1qEUuXvYLSah3RSd20DiUghUZFU5Kbza3vfIYkWsIaVVlSjClErOfTHPt3vww17/96SxkAKamXaRhRw4pz7Hz/2g6S+kUw5dp+mIPFArqdnUhqOjI/XMTyWJFwVO+hGkcSmEKju1CYeQRJljv9DMJNYS+twGIzax2G3yjKO4RqXo3FfSvjp92Bx+PG5XAQFByidWj12rEiC0uwgXNvHIjOIJJ8QRQKd2wZayEyFaz+M2w3a+dGPME2lC6ihqEtRCf7WsAKMvyncFNLjjInQWGdbOK907Bt/XMoHgtDx18PgF5v6LAJjdvlZf+GXPqM6yoSGqGWaKnpyPxsEUuAffnlVCf25PWPv0bv/ZyIYCvJyckMGD6CxB6pyKLL5LTEdPOtep6Ttr/2vtAwR4WX+L4n13dVlGTz0wePMGjyHLoPnFy7PXP/L4RGJmGL7HxDf70eN2rQj1iVqwkK7vg1SPnp5bgcXlKHx2gditCBiKSmo3KUQd5OGONfC9CpskKyqYK4XqM5nJ5OYXkF+Xv2sXHvfmSvB5vZREJCPP0GDab3oCGiC6WZunTvAUBJdpbGkXR8LlcF7iodIZF13/Ts5Xm8f++fcJRKHFr3H8ZesZX+42fx00f/5NDqApBUQrvKxPfrTq9R59B9wDnIusCv1SgvLkSSvYSF+cekmQWZFegNMhFdRQGwcJxIajqqzI2A6ldFwgAF1RLxVomJZ01kWrhvVfHSwkK2b1jLwbQ08otL2HHoMDvSM5C+XkiwXkdiXBwzrrkWo8mkcfQdX2ikbzbhkrzAmb6+rZQWHABFwhYdX2f78o8fxVWpMvPBW1j96Rus+XA5az5cDsCg8wdjDrZyePs29q9KY8+yg+gtLzFh7kyGndUxR/+0lvKSXACsoV00jqRpCjIqiEoMRtaJ1l/hOJHUdFQZa8EaDRH+NWlYnNnJ3sow9O8+w6y/PQ1AWFQUZ553IWfW7FNVWcHOjRvYv2c32fn57M7KpsemjQwff4Z2gfsJSZaR9Xoqi4u0DqXDKz028V50Su02Z3UZaavSSRkZR/cB55HS71yOHljNwe0riO8xhp7DpgEw4RJwuxwc2LaInz94l+1Lfgj4pKaizJfUhIbHahxJ01QUObBFi1YaoS6R4nZUGet89TSSfw1HveKe/xKps2M1N9zqEhQcwqjJU7j65luZe/0NAFSWl7VXiH7PaLZQXpCHoihah9KhleZnABB8wpt07pEtqF6Z1OGTAN/SAYm9JjLpsn/WJjTHGIxm+o6aRfLQHpRmOfB6Pe0WuxaqKvMBCAnzjxqVylIn1jDRuivUJZKajsjjgqOb/K7rCcDjclLsDSIqqmkjtmwRkQCkp6W1ZVgBpd/EKTjtdha/+B+tQ+nYVBWA9+68g/l3XMjCF//MivdfAiBlwMQmnyax7zAUt0z2gU1tEmZHodP7Gu63r/2EjH072b1xOZt//hi3x6VxZCdTVRV7mRNrmFHrUIQORiQ1HVHOdvA4ING/Rj4BFB/ZiYpMdHxKk/a3BPuG2x4uLsXldLRhZIFj8rU3EJWUwr41q9i1cpnW4XRYI865g0v/+TeGzhiONSKIjK2ZFB10AnBk9+omn6fbgClIskr6jpVtE2gHMWTcbDzlwyjzPEXa0YvJqfgTpd4H2bH+E61DO4nT7kHxqKKlRjjJaSU18+bNQ5Ik7rjjDgDcbjf33HMPAwcOxGq1EhcXx9y5c8nOzj7led59910kSTrpy+Go+yb3yiuv0K1bN8xmM8OHD+eXX345nfA7rsx1oLdA10FaR9JsRUf2AhDZbUCTj0mJDEPyejGaxCRpTXX5I09hMJn58fUXKMkVRcP1kWWZlH5TmHz5o1z10Kf89c1FXPOffzP5povoOez8Jp/HGpKAzuQlL31nG0arPZ3ewFnnfUCX4H8TFfRP4sKfBMBg6HiJQ2WpLzm12jpebIK2WpzUbNy4kfnz5zNo0PE33qqqKrZs2cKDDz7Ili1b+Oqrr9i/fz8XXXRRo+cLDQ0lJyenzpfZfPxN7tNPP+WOO+7ggQceYOvWrUyYMIHp06eTkZHR0pfQcWWsg4QR4IfDSAtzszDhIrgZqyKXlpURpPOv2iGtmYOCuPSBR1EVha+feFjrcPyCLMt0SRzMsMk3Yg6yNfm4fZv/h6daT1DX8jaMrmMwGM0MHDWHwWPmkjrgQhSvgaL87VqHdZLqcl+XWFCo6H4S6mpRUlNZWclVV13FG2+8QXj48YmtbDYbS5cuZfbs2fTu3ZsxY8bw4osvsnnz5kaTD0mSiI2NrfN1omeffZY//vGP3HDDDfTt25fnn3+exMREXn311Za8hI5LVX0jn/ywngagsLiEKIOjWesSVTrd2IKtbRhVYPr10w8BSBkyTONIAtumRZ9hDnNz9mXvax1KuzIYTEiOYbgNP2gdykmc1b6ibZPV/z74CW2rRUnNLbfcwvnnn8/UqVMb3besrAxJkggLCzvlfpWVlSQnJ5OQkMAFF1zA1q1ba59zuVxs3ryZc845p84x55xzDmvWrGnwnE6nk/Ly8jpfHV7RAagq8ruZhI8pqnQTaW3mhHqShCwaappl2VuvkLnrNxL7D+KsP/xZ63ACVkn+YXL3ltJ7wkCMpjCtw2lXuRn7wLIRj6PjdQsXZFRgNOswmsTknUJdzU5qFixYwJYtW5g3b16j+zocDu69916uvPJKQkMbnna7T58+vPvuu3zzzTd88sknmM1mxo8fT1rNiJjCwkK8Xi8xMXWHGsbExJCbm9vgeefNm4fNZqv9SkxMbOKr1FDGWpBkSBipdSTNpqoqhS4jUeFhzTrOqJOxV4si4aba+uMiti9ZjC0mlln/95jW4QS09d++jCyrjDr/Vq1DaXcHd3+PJCtEBN2hdSh1HNyaz/blmfQdF4ckPg0Jv9OspCYzM5Pbb7+dDz/8sE69S33cbjdz5sxBURReeeWVU+47ZswYrr76agYPHsyECRP47LPP6NWrFy+++GKd/aTfzdmiqupJ20503333UVZWVvuVmZnZyCvsADLWQUx/MHf8tVd+r6roKA5MRMZ0bdZxFqMRh8vdRlEFlvTtm1n+9muYgqzMfeoFsZZWG/J6XKSt2UXcoHBCwzvfWlDWEF8JQFKvvhpH4qMqKpt/OMwP83fSbXAUY2b618SkQvto1ozCmzdvJj8/n+HDh9du83q9rFq1ipdeegmn04lOp8PtdjN79mzS09NZvnz5KVtp6iPLMiNHjqxtqYmKikKn053UKpOfn39S682JTCYTJn+bej9jHaRO0TqKFik8tAOAqMTezTrOGmShtKqqLUIKKEVHs1j45KPIej1XP/FfjGYxm2pb2v7zu7gqZUacd5XWobS7jP1byCt5CdRkYpMGah0O9jInP727m8y9JQw/N5nRF3YXrTRCvZr1MW/KlCns2LGDbdu21X6NGDGCq666im3bttVJaNLS0li2bBmRkZHNDkpVVbZt20bXrr5P/EajkeHDh7N06dI6+y1dupRx48Y1+/wdVmU+FB/033qa7HRAJaJb8/4J2mw2vLKe/zz8EF+9+zbZh9PbJkA/Vl1ZwUf3/w1FUbjsgX8RFuMfU9n7s21LviekK3QfMF3rUNrd3j3PoKpu+vd/9pSt4e0hY3cRnz62gcKjdi66dQhjLu4hEhqhQc1qqQkJCWHAgLrzj1itViIjIxkwYAAej4dZs2axZcsWFi1ahNfrrW1diYiIwGj0Db+bO3cu8fHxtXU5jzzyCGPGjKFnz56Ul5fzwgsvsG3bNl5++eXa69x5551cc801jBgxgrFjxzJ//nwyMjK46Sb/WsX6lDLW+W79cNI9gMKCPMLkKgxBzWuZO/fSy+CrLzh0+Ai/pR/ht8PvIXk92EwG+vXrz4gJE4mI9o9F9trK8ndew+2o5pybbiehn/afnAPd0UPrKDnsZsyVEzR/U9eCopZglAeT0H2IZjF4vQobvklny49HSOwXwdQ/9BNDuIVGteqClllZWXzzzTcADBkypM5zK1asYNKkSQBkZGTUqQUoLS3lxhtvJDc3F5vNxtChQ1m1ahWjRo2q3efyyy+nqKiIRx99lJycHAYMGMDixYtJTg6gvu6MdRCWBLb4xvftgIrK7ESZmr8ekTUklEuvvR6A0uIi1i1fxt69+yj1KKz5bSdrtu/ArCrEdYlmwJChDBo9Br2hcw3lNJotAHQfOkLjSDqH3b9+DYDJ2jmnGpAlG25lD26PC4O+/RMJe5mTH9/YSd6hcsbO7MHQs5NE64zQJJKq1iyQ0gmUl5djs9koKytrdp1Pu5g/GaJ6wiXztY6k2TzOah6b9ySjYz1Mv6n1RuQcPZzO5jWrSU8/TKnDharTISleQvQ6zj3/AvoN7xxv8lt/XMTyt1/jorsfoOdI/5zDyJ9UlGbz5ZO3UnTIwcDz+3LO3M61zlbGgY2kZcwhmLsZfdZf2u26qqqStjGP1Z+nIckS5944kK49mj5RohC4mvr+3aotNcJpcNl9az4Nu0brSFokZ6dvyYquca3byhSf0o34lG4AeDxudmxYz6Z1azlabuezbxcR8t0i/vZ/DwX8KKDYHr0AyE8/JJKadhASFsc1j33Kx49ewd6VezhnrtYRta+k1JGk7T2TUvlNqiquIiik7T8EluZX8fPH+8jaW0KPYV2YcHlPsQyC0Gwiqekojm4G1eu39TQFGfsBlX5Trmyza+j1BoaOO4Oh486grKSY5/77AhUKZKenk9CjR5tdtyPokuIbvlqUFYDLgnRQOp0eVXUTHGXROhRNDBl5P1t3nMfGVS9w5vn/16rndtjdVBQ5cFS6qba7SN9WyKGtBVjDTVzw18EkD2j+ABNBAJHUdBwZ68Bsg+g+WkfSIvl5eYTL1Rit7dNUXFlWVnvf5fYtbldWUsy+7ds4fPAAeXn5VDgcXHDe+Qwa4/8tGzq9Hlmnoyw/T+tQOg1VVanIcxDXr3POhxIZk4px24U49AvIPTwXl91KUv/mJxsuh4fMPcXkHion/3A5Jbl2qivqzksVFhPEuFmp9DsjDoNRzBIstJxIajqKjLW+Vho/7UYpKLPTJaj9CvlsEREYFQ8uWc8HH3+CTlXx6Gp+nRUFIwouWc/RjCMBkdQAGC1B2EuLtQ6j07BXHMVRpqNLSqrWoWhm+Ph7+fXXH/n2lZ9xlccz/aYBdB9SdyRieWEVepOeiqJqohNCkPXH/4cpiso3/91GXno5weEmYrrZSOgTTliXIEKjLViCDZisBoxmXaccZSa0PpHUdAReD2RugAl3aR1JixU49AyKb7/J4IJDbdzytzt58dlnkSWIDrcRH59Aj7596dG3P6XFRbz06muYLB1v3ZqWMgcHU1lcpHUYncbRA2tBlYjv0XkXDLUGR2Ny3oCr3Fcr9+Mbu/jzi1G1NWxrvz7Ilh+P1O6fNCCCC/86pPZxYWYFeenlnHfzILoNimrX2IXOSSQ1HUH+LnBV+u3K3I7SfMrVILrEtu9QdFt4BP/3r/pHWlVVVABgsbR9oqUoCqgqsq7tms33rVtNaW4OBnPnrO/QQs6hHSCpxKWOanznADb+vD9xYM1CHKVdUbwqVeUuqspcmKwGfluRiSRBvwlx7FqVjeKtO5j22NjaiqLqRpe1EYTWIJKajiBjHeiMEDdU60hapOCAb0X16OReGkdyXLW9EgBzUOsmNV6Ph/S9ezi4ZzdZR7MoLi3DroBeVRpMsFrDT2/61k8bedGlbXYNoa7CI4exhEtiOQoMeBzB6AxuvG4D79+/BvWE6ajGzOhOvzN8SU1IRN2W0eikEPqMieWXT9MwBRnoPVrMhC20LZHUdAQZayFuGBj8s6ukIDMNCYWoHh0nKauy2wEIauHkaaqqUpKXS/qe3RxJP0Refj5l9iockgyyr0VG5/UQbDRg8Lpx6/SUFhYSFtX6TezpWzdRXVEOwNhL57T6+YX6lWaXYOsaonUYmtu7aTUeRwjn3RZJWXY4W5dk4HJ40BtkopNCGH5uCvvW5wDQJanu90uWJab8oR8Ou5stPx6h16gY0VojtCmR1GhNVX0tNYNmax1Ji+Xn5RIuO5q9PEJbctQskGmxBjf5mKLcbNb+tIz9aQco/92yaAZVwRZspXtUNIkp3egzaDDh0dEA7Ny0kS8WfceRA/ubnNSUFhWyb/t2crOzOHP6+YRF1n+co6qKr554GIArH3umya9FOD2K4qUy30vCAP+c3bs17dtwGGNIGMl9JiH3kxkyNemkfXb9kg1Ar1H1t8QMPSeZr5/Zwp41OfQbH9em8Qqdm0hqtFaaARU5fltPA1BQVk10O458aorqal9SExTS8Cdth6OaXRs3cnD/Xo5kZGBXZZBlLJJErC2USpeHs8+aTO8BAzGbG25FS+nVC/iOoxkZDB5z8gKr+Uez2L9zBxnph8gvLKTC6carO/6nV/re21x75z/qPfeKd14DwBYTS9eezVv9XGi5wqO/4XXJxHbrp3UomnI5yyk4GEnKUHeDE1wqikJeejm2aAtGS/1vKV1TbfQd15WVH+5Fb5AbTH4E4XSJpEZrtYtYjtY2jtOQ79AxOKFj1R04qqoBCK6ZCbW0uIj9v/3GkUMHySvIp6yqGjcySBIoCkF6PYO7d2fs5CnExjfv03lwqA3Z66EgL5es9IPs37mDrCMZFBYXU+n2oNQkMJLixSJLdI0IIz4hgdQ+/fn00wVkl5Q1eO7M3TsBuOGFN1vybRCaSVG8HN61hM0/LAAgvmfnLhLetf4nvE4bA87o1uA+h38rRPGq9BoV0+A+kiQx6eo+qKrK0nd246zyMHBSQluELHRyIqnRWsZa34R7QRFaR9IijtI8KtQgusR0rGZ6p9MBwKvPPk3liS0jiheTqhBhDSImJobk7t3pP2I05tNcuNCsk0kvKuXN9z4AQPJ6sOp1JHWJIiExkZ79B5LYI/WkT7thP35PYWUVjio75qC6MVSXl1FRmE9oJ1+hvD14vR6Wvnc/+1fvwG3XIRsVUsZ0JbJr526p2be+AEuYSnyPlAb3MQf7FrwsyKw45blkWeKsa/pishhYtWA/OoMsuqKEVieSGq1lrIMk/1waAU4c+dSxukYiu3RBTjuIB5m4iDDi4uPp0bsv3fr2w2Bs/VWHz5x4Jvv37iYpJYWeAwYSm5DUpPWoJk6axFeLFvPz998x7dK6dVU7Vi4DYNDU6a0er3BceXEWXz59O8XpDpJHdqHf+On0HHYxBmPnHj6fl5lG8eFE+p2pnLK4Ny41jMh4K4d/K+L713Yw/aaBDe4ryRLjL0vF41FY+dE+uiSHEJUgirGF1iOSGi1VFUPBHjjjDq0jabH8jAO+kU+pHWuCsonnns/Ec89vt+uNPmsKo8+a0uzjnNW+FqWwyOiTntu/1rdIaI/hnbsLpC0pXg8f3n8j1SUyU2+5hMET/6h1SJoqL86lpCCHzH3pbF8chjGkkpHnntPocbMfGMmXT27m0LYCFj63hYtuH9JgUi9JEhNm9yR7fwkbFx0+ZRIkCM3ln3PyB4qsjb7bDl5P4yjNx11dWe9z+fm5hMtVGCxNH2UkHBcT66tDyM/PPem5khzfiBIx4V7bkXV64vr7aju2fr+I4rwDGkeknaLcI3xw/24WPVfCoc0uAM6/eSjW0MbXc5NlmUvvGU5C33CO7ivliyc2+Sal/B1VVSnMqmDbsgy8HoXMPWLZD6F1iZYaLWWsheBYCE/ROpIGqYrCay88TaViJCXIQY/EWHoMnUh0r5FIskxeaRWxVpEbt1Riqm/CwsMHTn4zTR05ht2rlvO/p//F3KdebO/QOo0Zt85n26C3Wf7al7xz2x1cMe9B4rp37A8abWHlZyuBRADsRSGEJ2US1+2sJh8vyzIX3z6UH+bv4OCWAt688xf6jIklZWAUHpfCkZ2FHNlZhL3Mhd6kI7FPOD1HNlxcLAgtIZIaLR2rp+nAk1GVZ6dRqlgZHF5NhUNl2b4yftz3PaHSl/QIl8hxmBkfd3pFtp3ZsVqFUofrpOdiuvdk96rlOGvm3BHaTrcBUzAEfYWrEvR+Ognm6agoLSZ3dyI6YzXhiUUERyqMPb/pCc2Jzr1xIB/9cx2leVXs+iWbHSuPAhAeG0TqyBiSB0QS1yMMnUF8GBJan0hqtOJ2wNHNcPa/tI7klMpzfYvVjZ16IbH9z8BtL+PI5iUc2P0bBwuqcGIhufcQbYMMAF755HWjqspKABh23kXtHU6nUlJwkAUP34biVbnskX/QJXGw1iG1Ga/XQ3VlGcG2yDrbVUVBNlQj6x1cdteVyHLL3hpUVWXT4sOU5lUxZGoiY2f2oKLYgSRJhEa1bjeq3W0nvyofp9eJw+PA4XX4bj0OQowhjIsbJ2Yv7oREUqOVnG3gdXX4kU+mUN9Qc1elby4Vg9VG6sTLSJ14mW+7vQyjtfE+d6FhBtWLW9Lhcjowmo63EphqhnjnHUrTKrSAV5x3gE8eug2vGy594AESeo7XOqQ29c2rH5O9M4FRszyMnHq8ADg0IoqJV0ey8p0qvnz+Iy6945omjd47kaqo/PrlAbb/lMnoi7ozfHoykiRhiw6i4KWXOfj9974Z1E/40nfpQtI7byPpm/dWVFhdyMz/zaTUWdrgPpuu3oRJZ2rWeQX/J5IarWSsBWMwxAzQOpJTsoT5+ryrK+ov6BMJzek7+6wpLF6xkm8WfMKsa6+r3Z5zYD8A0cndtQot4O1e9wWOUpmhM4YHdEKjqip7N/9C/gHfZJQbvtDjqPwfE2ZcXLtP9wEDWcl68vcncvTQbhJTm/6/SfEqrPhwL3vX5TJxTq+TJtYrX7wY2WolaPhwX3e7JOE6fJjK5ctR7HZ0tub9H3lp60uoqLx1zltYDVbMejMmnQm7286sb2dxee/LRULTSYmkRisZ6yBhJOg69o/AHOab+K26suFZb4XTM3LimSxesZLD6YfqbB83+0rS1v9K3qHOOyKnrYV18b35dknuq3EkrU9VVfZsWkllqYusfTnk7EzCYJG4+O4uLH51L/mH69ZxrV28GKQwzrgS4rs3fdJBr1thydu7SN9eyNnX9TtpCQRVUXAfPUqXu+4iYu41tdvLlyyhcvlyX6tNM+wv2c/XB77mHyP/waiudac7eOjXhwgzhXHr0FubdU4hcHTsd9RApSiQuR5G36R1JI0yGE3o8eCwn3q2UKHlJElC73YRGhleZ/uhTRsA389AaH3lxUdY+9kCgiJV+o3x3wVlG7J99Up+/UgFDBiCQkgdn89Zcy7GYLBgsW3F4/HtV5KfxZpvV3J4UwxRPfIYPOHqJl/D5fDw/Ws7yDlYxnk3DSRl0MkLs3oKClGdTgwJdVtvpJruLbWZSc0zm54hMSSR2b3q/sx2Fe5i4YGFPDD6AWwm0YLcWYmkRguF+6G6pMPX0xxjkdxUV1drHUZA8xiMOF3uuhtrihxjU3vx/r//Sf6ubcQNG82Vd9+vQYSBJWPfMr599hnc1RLTb/tzs+tH/MHm70qwRFRxzvUjie8xCUmSURWFHR/9j/Js37ImL9+0HABJF0WPcXmceUnzJqxc9/VB8g6Xc+Gtg4nvFV7vPu6jWQAYEn63lMqxIt565rNpyOqjq1mTvYbnJz+PQWeo3a6qKk9seILU8FQu7XVps16DEFhEUqOFjLUg6SB+hNaRNIlFr1DtcDT7OI/TjbvSgckWhKw/eXSP4HNskrKi3w3rdtWsNL7szZcBkICc37a0a2yBavVnb+Ao1THr4ftJ7nvyyuqBQPF4CI83kJDau+axl6X/+oADeUmEmQopdfpaVUbNqqb3sDGERkSe6nT1KsispPvg6AYTGgB3li+pMf5+oVipJpFsYkuNR/HwzKZnGBEzgrMS6w43/z79e7YVbOPNc95E38KRW0JgED99LWSsg66DwOQfs/Ba9FDt9DTrmMLMfN55+23sqgOdKmOWDJhlExa9CYvRjMVsIblbMsPPD9zizKZqqPl97549tfcHz55LZWEBB1b8gNfjQdfM0SJCXROv+CtfPPI4Kz98jsvu60lQ8MnLVPi7biPt7FuZQFlhEaG2UH75z/sczEvi3Gnl9Jg5m5WPvc6erG6MOOs8JLllQ5/LCqpI6n/qxXhdmZnoIiORf79obM011Sa21Hx94GsOlB5gwQUL6gzVrnJX8ezmZ5maNJXRXTvfpIlCXYHX5uoPMtZC0lito2gyi1FHtdvb5P3L8kt4/5130aky54+cyqQBYxmY3I/4yK5YTBaq3A4OFB5h2caf2zBq/6HT6UiO8NUAZB06CEBFWSlZio7quG5c+q//MPXS2YTHxCKpKr8sWqhhtIEhIXUCE6+/gKJ0O2//bS65h7drHVKri09NAVXHrnVrWffi++w80o0zRhfSY+YMvA4H+44mMqhXbosTGle1h+oKN7boU88/4846enLXE8cnnmxKS43dbeelrS9xYfcL6R/Zv85z7+x6h2JHMXeOuLPpwQsBS3zca2/lOVB6xG/qaQAsJiP59qbV1FSVVvLB6+/iUbxcd821RPeIq3e/VZ/8yPJ9a3n+0f9w3Y3XY4s99ae9QDf53PN49+NP2Ll1Cwnde/C/Tz4CnZ4rb7yJlF59AOg3agybPnmHA5s3MGnGLI0j9n/DJt9CeFR/vnrsPxzetYzYlIYn3asoySZj7y8UZO4lqe84zCFB5B3eidvpYtS0jjnSxhYRC+SwdVEQ0I2Y6GoGXXcFAIW79+NRzfQY3HC30TGKV6Gi+OTu56Kjdt91ooNOebw7KwtjfMLJT8hN7356a8db2N12bht2W53tOZU5vLPzHa7tfy2JIYmNnkcIfCKpaW+Z63y3if6T1ISHBrM9X2XLwlcYNuPmBvdz2Z18+PK7lHvtzJ11dYMJDUCv4f3YdXgfec5iCo7kdPqk5vA+X1dTeUkxXq+HA7kFAHTve/xTqS3SVwNhz89r/wADVHTCQCRZJXP3DsacUCOreL1sXvYqu35eRXmuHbf9WGuGymY21jlHUp8xxCaPbLMYd2ds48Nf17PigAWXYiTCbOdPE1KYPe7Uq8LHdevLpOkLObwczFE6Jt9/t+8VqCoHVmxBL8UQPf7sRq+/duEhti3NqPc5WZawdTl1S40rKwvLsGEnP3GspqaR7qdcey7v736fuf3mEmutO1z82c3PEmoM5YaBN5zyHELnIZKa9paxDsK7QYj/LOQ27pKbKHvr33yzLZ/MjHs44+I/EPm7eT28Tg+fvPguea5irrpwNgkDUk55ztheiVx+1RxeePsVXPWse9TZKDUfVvsPHkzmAd8Mwlap7idYo8mEKkl0qWm5EU5fcHgkPc9MIe3nwxw9sIX41GEc2bOCH157jspcBVsipIyIo0tKbxJ6jSY6YTD7Ny1EwgSyzI//fY/i3LRWT2rKq8r4dM1Svt5Wwu7COCz6KMYk2dmerZJWEss/vnHw1ZY3SQjTce9FFxEVenKRb1XaV/T+7UkSe0UT/OffkHW+JGL1cwv4LS2J7l0y0ZkbX+fKaXcT3tXKmXN61W5TAVknERRixGw1NHis6nLhyc2tt/uJY71Pyqlbav675b8EG4L548A/1tm+OW8zPxz+gX+f8W+sBrH+nOAjkpr25mf1NAD6oFAuvPVJwj59gZ/2FFP2yXzm3vtc7fOK28sXL37I4epcZk25mO4jmvamawzyzb8ikhrYn54OQGh4BG+/8y4YjIwaefIbpRwUgr20pJ2jC2xTr3mQzO1/4LsXH2HUJeez4o2FmG0w7W9XMGDMNSftP/CMuQDkZ24FwNuMerPGrN23mXdXb+fnQ2E4vBYGxVTw6Hl6Lh11AVaz7+8ltySb2z78gaIqmQ1HI/Goi3n+mrpxOgt/Q//Fn3AEWwn6w8/Iel9rSvmhQ/y2Pwa95GLS7Rc2KSZVVTFZ9MT3Pt5V5ah5zWZDw6MaPSUllHz4EagqxoSTu5+k2mH0DSc1uwp3sejQIh4a+1CdxMWreHlyw5MMjBrIBd0vaNLrEDoHkdS0J2cF5O6AEX9sfN8OaMLltxH61XN8/Rus+/Axql0eErr1Zv/GI+ypcnDhuGn0nzikyeczBfn+0bqczjaK2H+UFxWBrOetDz4CvZ6ByQlMnH7ynCGmkFCqS05OaqrtdnauX8PWJYsxWIK49sF/BeTcK23BYo1lyp9uYNGT7/LTK98Q2SOIOQ+8idkadsrj7OW+pUMsIa0z0durS77kyeVmIi1m5gyp5JoJw+gRe/LvQGx4HJ/dej0A189/g13ZJxf62tc/QYTTg+Pce9Fbj3cDb1mwHIscxTVPTMIQGtakuDxeFbUm8VAUlbu/2M6i7TkkRlj431/PINh0/G1EsdspfH0+qtdD2Rdfojgc2C65xLc8wu810v2kqipPb3qa1LBUZqbOrPPcwgML2VO8hw/P+xBZEr/nwnEiqWlPWRtBVfyupeZEg2bczv7MB/nh2Mz9GfsAGOUJY/i05r0uvdmApEq4fj/pXCeTn32UqmNza0gS1119NcmpPevdNyg8guL0AyxZ8CHOqioqigopOJSGu6QQqZkzswrH9R42i6yLt1JRVMj5f/4vBmPj3TJZab8CEB0/tFVisNZ0BX31l3Ekd0lp0jGRwWaWH4rgqpdf4+5zRzK0hy95CJ38HJX7ViItfhJH3NmYY/pgzzzC3owERgwpaVJCU1ru5I2Xt6DPqCLfCD+8t4kKh5v16cXcNqUnb69O55FvdvH0Zb4Ca1VVyXnon1QsW4YcGoL1zInE3HMP+sgG5r9pZPK95ZnL2Zy3mVenvlpn7pkKVwUvbH2BC7tfyODowF1RXWgZkdS0p4x1YImAqPrfsPyBJMtccssjDN34A9XlpSxd9xuyYmGwvvk1QpIsYUCH29W5u5+iYrvW3o806RtMaABCo2Mo3vMbO75eAICq02GJ7krChKn0HT2Wn955Ha/LJVppWmDKlf9u1v6HNm7BFq/HFtU6o27O6j+ChxZvYvmurVzXxKTmHxfMwKv8j8V7uvD44t/4/FZfUqMPiqF4ymssfd2Ddd46LnmkK7+8sRy9HMXAOec26dxrNmQTdKSaikQLVXFG7E4P1W4vr18znGn9Y4m0Gnnk210MiLeRlLGH1G2rqPzuO+Kfe5bQ6dMbPf+xoeT1zdPk9rp5bvNzjIsbxxnxZ9R57vXtr1Ptqeb2Ybc36XUInYtIatrTsXoaqWXzQnQUOr2e1LG+fux+UzyUPP0miqdlMwbrJV2nb6mRZZlbbvwTudlH6T/s1LNMn3HRTNxOBykDBhEaHkGvocPQ632Fmh63m+8L89BHdkFV1ToTlAmtq7LsKEXpLoZc0HoFwgmRMfSMyOPHXRLXTW7aMdGhITx79dXwwdt8syeCzMJcEqNiKSnI4scP3LiUcByOMD6+70fsSjLTplVgCmt8GDdAz2420oHuA6O496JeJz1/+chEPt+cyWNfb2Pht/dTGhFJ3N/vblJCA9QO6fbkFyAHWZF0Muh0SJLEF4e+JrMik2cnPVvnkPTSdD7a8yF/GXIzMVb/GWwhtB+R1LQXrxuyNsOke7SOpFXp9HokVYckN339lhMZ0OF2d+6WGoDouHii4+oZIfI7MYlJzLmz/t8hZ81SFp6ifN588B7+9NhTrRqjcNxvv3yI6pUZNOnKVj3vlJ7w1sYIyqoc2IIa7wI75t4Lp7Fw92Y+XbeOv559Nt/NX4biiWD2Qz1w7NrPkq/NTJ5YTOrMps9vlNo9nK8NUL61AOpJaswGHYtunUBpYQk536rkz/0LA//o+37kpmdxYNVGYgf0JHX4gHrPL1t8NXUZ11570nNDgJtvm0Cv8BNGXKkq+66/htvNeuZcObfJr0PoXERS015yd4Db7tf1NA1SJSSpZfUcelmP261tS43T7uDnj3/EGhbM8OnjMAefet6NjsoaEsIfXniT9/5xG+WZh7UOJ6AdWL+BkK4yUXG9W/W8M0cM5bX12XyzeQ3XTDir8QNqdAmLp1/Ud7y0Op51P/3M5OokJl1nITouGeKSuX5q81vuJEnCG25EKjp1Ib+upsWl/Ne1uP5wKS6Hk/2XzSG6sgg3cPSb74nvlXLScaa+fUn+5GOUSjsoXlSvAoqXfY/ch7Wgkhnn3VFn/41fv07y7iKSAaMH8e4l1Et0vLeXjHWgN0PXwCtsUxUZ5JYlNQZJh8ujXVJjLyrn7edeZ13WNpbtXM1nr36oWSytYf0P36E6quh15lStQwlYVZUFFB6sptvw/o3v3Ey9EobQMzyb737Lavax7984i2ndjzKh2oQ7pIL+o49/gGpJV2RJSTXmfCfmbiGn3C8kwsahS/5Aj03LWTFtJj/d9HdCq8rIvP4OAMryCus9TpIkgoYOJXjCGQSfeSYhZ02muIsFU1El6ZePIyalX+2+Xq8H+4uvUR3k6+au3iIWdhXqJ5Ka9pKxFuKHg96kdSStTlVlpBYmNXpZj1ujpKY4q4A3X55PmaeSay++kkl9xpBeeRSPw39rfAoyj4A5iAuvv1HrUALWzl8+8nU9nXl5q59bkiTO7qNn01EbpVX2Zh0bERLBqzfcQGUEGCpCePyulaRnlrcojn/fv4qP71uLAZg2I7XR/c9//B48z7+OuaqSXttWkXXRlfScNgmAo5t+a9I1VVVl/8P3UmrTceY/nqnz3C/v/JvYHCe6px9CFxWFfe265r4koZMQSU17UFVfS40frffUHKrS8qTGoNPj9jZvBfDW8s3HX+FSPPzhqmtJHtaT2KQ4VAmKMvxzGQJHdTUFO7edMKmZcDqUBoYap2/bjjXaS0zSkDa57iUjR+FR9Cze8muzj5Vlmdvun0B5tAGbXeGH7w82eozT4eHL7w/w/me7eeqpdTzx8GrCij04JRXjmGj6dmtaYfGQaWcw9MdvybntAaY9chfdBvcmre8oQt59laKj+Y0e/9vXb5GwuxDXLVdhPWGOIGd1Jfq3Pid9cBcGT5mNdcwY7GvXNikmofM5rf9+8+bNQ5Ik7rjjDgDcbjf33HMPAwcOxGq1EhcXx9y5c8nOzj7led544w0mTJhAeHg44eHhTJ06lQ0bNtTZ5+GHH0aSpDpfsbGxDZyxgyk+BPb8wKynAVDlFv8m6WVtkhpHZTWZ9jxG9BxMbE/fbKfHhpa6nP5ZuJy2fQuSqiAbjFqH4rcUr5cty97ihT9cwHNXXMRz10znh3dv47On5vLWP2bw/NzpZG0rwGxreGmA05Ua158ISwXbjxxq0fEhwUbu+9cEHLJK8ZGKU+7rcnt59t5fyP1fBhXLczGm2wnJ9f3+j7yqNzf+YVCzrh0aYeOsm6/GYDJiLywm9MyJ2JyVrP73cw0eU5xzmCWvPUDp089xoFcwk678R53nf37xAcJLvfS69xEArGPH4Ni1C29ZWbNiEzqHFpdabdy4kfnz5zNo0PFf+qqqKrZs2cKDDz7I4MGDKSkp4Y477uCiiy5i06ZNDZ5r5cqVXHHFFYwbNw6z2cxTTz3FOeecw65du4iPPz4ipH///ixbtqz2sU7XsmHE7S5zPSBBQtsteqclVdUh6ZqfmOQeOsqhqqMM1GDenn1rd+CVFPqNGghAZVE5i376nggphK69/HO132Cb7xP12Mtad0ROZ7F301f8/P47VOap2BIVPA4D1aVedn1/CIPVS2islfgBNjI2FxF2wtxCbaF7uJMD9ZeiNJm3Tyhhuyv4/oeDTJ6YhDnoeCLm9ijM/+tKAEIBJSmI867qQ4/kML78eh9dE0MYN6LhBWmbYvnN/yD1t18pCI6kx+yZ9e5zdM8mMq67nsRSN5VmCHngQXTy8f/r5cW5hC1YyuHx3Th/6CQArGPHgqpi37CB0LMbX5BT6FxalNRUVlZy1VVX8cYbb/DYY4/VbrfZbCxdurTOvi+++CKjRo0iIyODpKSkes/30Ucf1Xn8xhtv8MUXX/DTTz8xd+7xoXt6vd5/WmdOlLEWuvQDS5jWkbQNVaa5M5W7HS6++PgzQiQL5159UdvEdQr7du8lXAompqaVZulHi3Cqbq675lr0prb7FN6WwqO7ALDhf18w+uymTbAm+CiKwpKX38QYLDHtjjn0H3M1kiShKAoHt/5EQu9hWIJ9M+O63VXodG1bG5carWfp/lMX6DbmzluGM++BX3AvPMzBhYdRB4Vx682+yfl+21VQu59+WAR/vnFI7eNLZ57+iK6M7XvQZR0hKzqJM5d9i9FUf+vhtmceIr7SjfvNxxk+8jxkU93v64aPn6drtUrCfU/UbjPExWFITqJq7VqR1AgnaVGnwS233ML555/P1KmNj7AoKytDkiTCwsKafP6qqircbjcRERF1tqelpREXF0e3bt2YM2cOhw6dunnW6XRSXl5e50sTAVxPo5SX4XbHQTMbzb5760uK3eVcctFMzGHtu8Ku1+PlUEkWPWKSASg4nMuOojRG9xxKZA8/TJprhNb8vbgKcvGIuX+aJSd9E+4qmbGzL2XA2GtqRwvJskzP4WfXJjQABkMQstz6rcSH8o+yZPtG1uzbjtWkp6g6jKLyxmtRGqLXydxw1wjswTokJMp2ldR2sQ4dEE2VDhzdg+okNK3huydex375JcSX5lI9fGyDCY2qqkRvPULmOQMYdMbMkxIaAE9ZKQ6jRFyPut1g1rFjsa8RdTXCyZrdUrNgwQK2bNnCxo0bG93X4XBw7733cuWVVxIaGtrka9x7773Ex8fXSZpGjx7N+++/T69evcjLy+Oxxx5j3Lhx7Nq1i8gG1haZN28ejzzySJOv2ybshVC4Hyb+o/F9/ZBry2bAgDGxacWEAFsWr2FbwT6m9BtP0tD6R1Z4XG4K0nPJP5xNRVkFY2acid7YOi0oR7al4cBF3yG+Ibmrl/6MCQNnXNT0eUE6kuoyO1m7D7N+yTe12xxVVQTbRG1NUx36bRlIKj0GNXE23DZw4zs/cqDk2Cy5wQBkFuUSGdqlxefsGm3lgf9M4otP96CsyCa3pJquEUGs2Z6LTlGRDK1fVK7s2wNA7PKfGRwb0eB+blc1IXYFzy+7cbscJ623paoq8urNVIae/DYVPGECpQs+xZmWhqmn/y47I7S+ZiU1mZmZ3H777SxZsgSz+dSzXbrdbubMmYOiKLzyyitNvsZTTz3FJ598wsqVK+tcY/oJU28PHDiQsWPH0qNHD9577z3uvPPOes9133331XmuvLycxMR2rpfIXO+7DdCWGndGPhKRWJo4NXpuWhbfr/+JnqFJjL9sSp3ndq/ezpZ1GymqKqXMa0c5YUK/mMSu9BzbOvOC7Nm6CzNGUob3RlVVDmSn0zs8BXOIf02653V7efeZ18l05CN53FgP7kQ2mul7zvkE28K0Ds+vHN2zG2s0BNvatlbmVHpGVXGgBD7/YxfsLjd6Wc+g5Ppn422upG428lbksO9ACetLczj49WG8JplrLu/X+MHNZD7nXFj7I2V79xMT2/D/vZ93LiIBCK9Q2LF0AcPO/0Od5zet+ITE9EoyLz35HMETJqALC6N04UJi/v73Vn4Fgj9rVlKzefNm8vPzGX7CMvJer5dVq1bx0ksv4XQ60el0uN1uZs+eTXp6OsuXL29yK81//vMfHn/8cZYtW1anALk+VquVgQMHkpaW1uA+JpMJUz1Nmu0qYy2EJkCYfxafNsad78RgKkDSN605fsfqrbglL7169qyz6KK9tJKFy74lWLaQGBHHsOhouiTEYrUF8+YX77FyxUqW/fQT3eOSmXb9xacV84Hcw3SzxaMz6MjYdQi76qDvoNb/597a8g8cZcsvmzhw9BBjBo/E7XKT6chnSt/xZP+6nDz0/GHey4QmiDVxmqvwcDFd+2rb9VjuNNEvuoCRPc9v9XP3TA1nI3BwTxFl6wswqxLXPDqO0NDW//84YeZktj8RTNaChfSa1HBSs/7lh0kAcv5yEWeee/VJz7vLfaObXPWUGUhGI6EXXEDZN9/Q5W9/Q9KL6YUFn2b9JkyZMoUdO3bU2XbdddfRp08f7rnnnjoJTVpaGitWrGiwa+j3nn76aR577DF+/PFHRow49aJ+4KuX2bNnDxMmTGjOS2h/AVxPA+AuM2OMqG7y/pPmTCPv5Xy+2/wTTqeTMy7zdTH+unA5XlVh7nXXEpYYVbu/x+UmWh+GW3FT5XJwMPvwacWbfySHEm8Fk/qMB6A0uwgAS5emd482laIolGQXkXsgi/yjuRQWFlJYUdKsxMzr9bJ71TY2rN9ApuP4/DnLN/8CQK/QZBLjI9iQt4MzzrxCJDQtUJyXhrNMJqFv84YvtyZFUThabsKib5taqPAIC0XhOiLXFmJCotQstUlCA2A2mygcPIaoTb+iKEqDK8afdfFtsOa/xI2ehE538luRt6YG0jyg/tYq28wZlHz4IfY1awieOLH1XoDg15qV1ISEhDDgd79gVquVyMhIBgwYgMfjYdasWWzZsoVFixbh9XrJzc0FICIiAqPR18c/d+5c4uPjmTdvHuDrcnrwwQf5+OOPSUlJqT0mODiY4GBf3/Ldd9/NhRdeSFJSEvn5+Tz22GOUl5dzbT2LoXUY7mrI3gaDWn/m0Y5Adblwu6OxxjR97KnBYuTKv13HN699yrJdq6koq2Da9RezNyONnmFJdRIaAL3RwC3/dwcAC19ZQEbh0RbHu+WHtaza8Cs6Vab3WN9Q7u5De2P7dQUff/EJfwy6nujuze9+UBSFouwCcg8crUleCiisKKHEU4EHr+91qDLh+hCcipt9WQeY1sg5K0sqWP/9L2xN20GlWk0XXRjTB08mZUAP1i1dzdb8vYRKQUy9+By+/fdDRFrjGfGn2c2OXYAD234AIHWIdiPGnvz2Gw6XRvJS09ebbLZb7h3NGw+sIcQDU/7Qp+0uBBhSkumyYRlf/O1fzP7vP+vdZ/g517Dzof9S8N3n9Blzcvd1+aF9RAHG5OR6jzf364epVy9Kv/5aJDVCrVZts8vKyuKbb3zFikOGDKnz3IoVK5g0aRIAGRkZdbL3V155BZfLxaxZdf+i//nPf/Lwww/XnvuKK66gsLCQ6OhoxowZw7p160hu4Be+Qzi6BRR3wLbUeA7sBwwYUpqXCMg6mYv+cjkhH3zPL+kbKXimkGKlgjP7nXHK44wGI2615RP1LV2/ApsumAvOnl474io4KpQb/nIjr776KqsXrWDmbQ3P8eL1en3Jy8Gj5B/NoaCwkKKa5MWLb/ZZg6ojXB9ClDWcvpE96RIfQ0z3BCKTuyDrZH56/zvWH9ra4DUydhxi7cpf2V+UDir0DE1i9ISxpIzsVTsi5+Kec7jA40Wn17Hm1fcps+cz65ZH0Rn8cyi61jJ3b8NkU4iMbds3+oaU2st5ez1M75XBBSP+0mbXibSZ+dNjY8nKrmRwv+g2uw5A9+lTcH/2Fn2WfI6qPlTv2lMmi5XsgbFY12w76bmqkgIsq7cDEJVaf0uNJEnYZsyg4Lnn8JaVobPZWvU1CP7ptJOalStX1t5PSUmpHTLY1GMADh8+3OgxCxYsaGZkHUDGWjCF+uaoCUDuA4cBG4Y+zX8zkGWZKdeeT/DCEH7YugIdMr3Hn7r532QyUaFW89TD8/jLLX8hJDqs8RirXRzYsJu0PfupVl1MHTyc1N8VHIfEhNEjJpn9uYfJP5RDRFI0hTknJy+lngoUfL/fJlVPuD6UGGskAyJ70yU+lpju8YQnRSGfor7IFhGG65CHqtJKgsKC6zyXuzeTd7/4ADNGxiQOYfR5ZxAaV//oEZ1eR1lWDhtXLaR3t3EkTRza6PdCqF9pThFBYdqNFPti3UrcigG3N6jNrxUZZiEyrO0L4lPHDmXJOZdiWPPzKRfTDJo8ia5PLyD/0C66dPf9XZblZrL16pmEFFZR8uK9jBtzXoPH2y68gPxnnqF88WLCr7iitV+G4IdEdVVbylgHiaOgDea06AjcR0vRyR7kiJZ/6hs9YyLhXSIoLy7DHHzqf7Yjp42jemE1m3J2Uppd1KSkZuH8T9lVcpAgTPQN60afCfWvkn7WRdM48uZbvPrefJBArUleLKqRcH0I8dZoBkf2pUt8LLHd47ElRiIbmv9zDY/2DX0vyS6sk9QUHMxh5aKfALj9ztsx2U79BqeqKj8+8zx6ycjkO/7c7DiE41JHDWPT5xtI37mEbgPOaddr7zuaxmNLfb9HZoO3Xa/d1jzFxbitp65VG3ThteQ+s4ANb87jgsc/JP/IXvZeMwez3YXulccZOa7+mYiP0UdH+4Z3f71QJDUCIJKatqN4IXMDjL9V60jajLtIxWA9/QkNe41r2rDV0Jhwhk0axaZPduJyOJt2cgmi5FBu/r87GixYBAiPj+Qvd9zCliXrUKo9xMTF0qV7HLbEyFadyyMszpcAluQVE98vBYD3n3qDQ1W+WqFu5q6NJjQAu75bSmb2Ls6ZfhNBp5gLRGjc+IvvYddPl7Di/VdJefLsU7YstLZ9OTkAPHmhzKVj5jayt3+RSotxhoSdcp/oLilsvmgEyQs3sarPE8ivfoROUQh76yX6DGnavFG2mTM5evvtOA8cwJTa+IriQmATy/m2lfw94CwL3EUsAXeVDUPTBre1GpPFN3fR0UOZeJzuRve3Wq04FNcpE5pjLDYr4y+bwoS50+g1dTBh3aNbfXKysNgIJFWirLCkdltRdSkAo+IHMee2phW+r//qU+LDejPgmoab5oWm0RtMjJ51ASVH3Oz49YN2vfbIHr6u6SeXlqILsNXVDeWlqLbGJ+Wc8vB88uIsRD/+HooMCR+83+SEBiB48iR0NhtlCxe2PFghYATWX1FHkrkOZAPEDdM6kjbhLSrAq4RjiG/6TMKtISQqlGAsLN+3licef4J3n3ydVQuWkJuWhaIoJ+0fHBpCtepCcXWMpn2dQYdVMlFWenyF4ZhQ34ivUeeMw2Q99aSWANVFZZTa8+g1bjySTvwJt4ahk/9MaLzMLx8s4Lv5t1KYvQNFaZvV40sqS/hszVJuevsDzn3e1+U4KqGIJpQj+hWLvQwpsvFWRIPZQs8XX+XgxO70/uRzknsNb/SYE8lGIyHnnEPF72o1hc5JdD+1lYx10HUwGNu++E8L7r17ATCkdmvX6xqDzNz54N/J2pnO/m17SD96hJV71rJ87xqCsZASEU+PXqn0GtUPa0QowWEheCWF6sIKrHFh7RprQ4INQZRVlKMoCuW5xTjcvq60fet3EZXc+ARwRosFCRmPS6zv1FpkWebsG25n6RvPs/endPb+dB+y0cv022+grCCD8sI8DGYLBqMZZ1UFOWkHmPX31zFZmjbi5lDeUT5ft4pfDlaxpyAar6ojLtjLlNQyrpvYj4HJrT/hnpYURSGkuoLSqKjGdwaS+44mef53Lb6eedBASr/8EqW6GtniXzODC61LJDVtJWMd9Du9mW87MveRXCACfY/TX9G3uWSdTNLgHiQN7gGAo6KaAxt3c2BPGoeLMtm57gDS2h/oYghHVzMSqaKkvMMkNaHmYA6VZfLko/NwcrwLLTSiaW+QklmPioLqDbCP9hpL6TeFPz03hf1bFpJ3ZBcbFqzlu6ffAUA2KCgeCdTj9TZZab/SY1DTuv+umL+S4morg2OruH2ii3MHDaRn3HntWr/TnsoKSzAoXoJi2nbo+DHmPn1BUXCmpWFpZDZ6IbCJpKYtlGZCWWZg19PkVmEwepFaaZHJ02EOsTDgrOEMOMvXbF14JI/9G3dx6PAhDlRmAWAK6zgtZkOGDYGtEtERkcTEdSU2NZ6o5FjkpnYlKSoWfQgbV32FZJAZPvdSdAbxp9xaeg2bQc+hF7NhwYUAnH3L5QyaeA2KouD1VFOce4AP//4ARUfT6NHA+2dFVRnfbP4Vi95JVGhX8uxhTO2Ry5t/+mM7vhLtFGb4CqBDu7Z8Mc7mMPXqCTodjt17RFLTyYn/hG0hwBexBHCXGTGEOrQOo15RyTFEJccwjrPwerxUFJQR1rXjjBDqN3kY/Sa3vNZK1uu48tGnWfnKG/yy5AN2rP6RM6/4I6nnjG/FKDs3SZKY+X+3E50wgJBw3+SSsiwjG63EJA1GZ1Iozs2o99iXfvic+WsUyl3BgBHwLcXhPbnkK2AVZ+URCkQktM96WrLJhDE5GeeBA+1yPaHjEklNW8hYC5E9wdq0/mR/o3o8eJzRBKU2fXkErej0ug6V0LSWsB4JzHjmETJWbWHl+2/yv7fmkbioL5Nvuonofj20Di8gdB94doPPWcJkyvPzT9r+8erv+M/KIKb0yOOuc4dS5axAJxsIsYSQGDW1LcPtUMpzfElNdGL7rXpu6tFDJDWCSGraRMY6SBqtdRRtxpN+EBUThhRtVzUWIGniMK4e/xI7Fixm7fef8MEjf6Nfv4lMvOUGgqLCtA4vYFkjrBzdWcQbd16IyWrikrteJLOynEe/dzAhuYQ3/nh9k6YRCFTV+YV4ZB3WqPYbHWkZMpiCF19CcTiQzY2PIhQCU+f9q2srjnLI2wWJAdz1lHYQAENvbdbKEeqSdTKDr7qA6197g+EjL2Dfnl9567Y/Ubg3vXafqqJSDixZw+r/vs1Xd/4fb1z7Bza/+6WGUfu38Zf9kZSRCRiDzBTsd5C2dxV/+WAtYeZqXrjm0k6d0AC4CgqptIS0ayF08OTJqA4H9rVr2+2aQscjWmpaW9ZGQA3sepqsYmTJi66LaKnpSIzBQZx5940MOXIhb/7jT6x49XVkWUdhUQaVTt9kfzrJQERIHNXOcvIPHdQ4Yv/Vrf85dOt/Dju2LGHJky9w46fVlFrC+OT6FMKD23fupo5m64+ridzwM/bQ9p2Z09itG4bkJCqXryBk8uR2vbbQcYikprVlroegSIgMjOm6VacLb3YGusRuSDXDo92FCoagUm0DExpkS+5KqDmK7Lz9RITGkdxtMDGpqcQN6ktU/x7o9Hre/MP1ATucuD3Z3b66uRmDFOZMH0KP2BRtA9JQSV4Rq/7xCKnrl+GJTiTpsUfa9fqSJBEyaTLlixejer1IusBcc084NZHUtLaMdZA4GgLkDaP8/c+pOJiExEEMlkKMUV4c5d0ITkpv/GBBM9fNn4+s051ixXAxx01rODaiafKAkZ02oVEUhZWvfoTljRdJ8LjIuOJGzr7/r+g1mGYg9LzpFL/3HvbVqwk+88x2v76gvc7d8dvavB7I2uRLagKEaq8CIKR3ETqTh+rsEABMPWK0DEtohN5kPEVCI7QWr9e3/IZO7pzf60Pb9vL99Nl0ffFxClP60PXrb5j+zzs0SWgAzIMGYerXl5KPP9Hk+oL2REtNa8rbCW57QCU1+thQyAXrheegq5nyXK2qQgrqOJPZCc2nqmrAtCZqyVPTVKPXd67Ph46qapY+9B+SFn9GaJCNkv97gguv1n4GdUmSCL/iCnIf+ieujAyMSUlahyS0s871l9jWMjeAzghxQ7WOpNXoQo0AqCes7ScSmsAgIZKa05WVmQ2AvhMtLLrx66WsnTydlO8WcGTyRQz76QfGdYCE5hjbBRegj4qi8OWXtQ5F0EDn+UtsD5nroOsQMATOHAn6ZN+ClZ592zWORGhdoqXmdH3z/c9kL3qPysju9O/TXetw2pzL4eTru/5F8H234QwKxvDWR1z08r+x2oK1Dq0O2WIh6ua/UPbNtzj27dM6HKGdiaSmNWWsD7hJ9/R9BqHX5VGxKkfrUIRWpCJymtOxbMVa9rz3LM7IZP7+nyfR6wO7J3/z96tYddYF9PzuE9LOvJCzlyykz7ghWofVoLBZszCmpJDz4EOoHk/jBwgBQyQ1raUsC8qzAm7SPbWqEn1QNU57AqrTpXU4QmtRVRDdTy1SWmFn7RvPUWWL566nniAoyKJ1SG2mKLeIr669HfPfbkLV6+DVd7jo9ac6/AKqksFA3LzHcezcSdE772gdjtCOOvZvpj/JWOe7DaAiYYDS177GUZGMbWA+ksmodThCaxI5TYvs2XuAIG8VXadeQLA1MBMaRVFY+uonhLzxAikeJ1lX/Zkp996i2aimlrAMGULk9ddR+MKLhEyahKlnT61DEtqBaKlpLZnrIaIHBEdrHUmrcpUGYw7NIOSq2VqHIrQiFdFS01KSx+27owTmstv7t+3l2/PmkPTiY5Sm9CZu4TdMe/B2v0pojom69VYMSUkc/fs/UBwOrcMR2oFIalpLxrrAWxrB60Hx6jDEdKxCQKF1iJSmZUaNGordEMKu5T9oHUqrqrI7+PLv86i6ajZRBVlUPDiP8xa+T2yq/w6Llk0m4p99BufBgxS//4HW4QjtQCQ1rcFZ6ZujJnGU1pG0Lp0eWfagup1aRyK0NhWxTEILyTod1XH9MFbk++b7CQDvrFjBz1Om0/vbD8g98zyGrfiRUVfN0DqsVmHu3ZuwSy+h+P33UV2iLjDQiaSmNRzdBKoScEXCALLJiadY/CMINKoY0n1azIoDNTjC7xPD/fn5nPnfD3hxYQ4ppblk/3kO577yBJbQEK1Da1Xhc+bgLSzEvmGj1qEIbcz/Okk7opzfQG+BqF5aR9LqzMl6KvZ2Qa0sRwoO1TocQegYFAX8eGkEt8fLPYsW8/UGD5Js4PJpMo7loFQH5tQNpt690UVFUbV+PcFnjNc6nNOmqiobD5ew/lARBwsqKbK7kCUJvSzRLcrKgHgbI1LCSQjvfBOliqSmNYTGgacanGVgCdc6mlZlGTeM8r25OH5dhWXaBVqHI7Qa1e9bGTSlqqiSfzZ0f7d7F/d8tY3KylBSkwqYP+dCukd0Zfl/5uE+kqF1eG1CkiSCRo6gavNmrUM5bct25/HED3s5kF+JzWKgZ5dgokNMqCq4vQo/7s7lzdXpyBJcODiOOSOTGN0tAlnuHH/vIqlpDTH9fbd5uyHF/z8FnEjfMxWdfjeOHXlYpmkdjdBaAqQURDuqF/wsqcmvqOBPC75l+8EQzEEu/jXbxjXDLqx93hEXgeFogYYRti1Tz55UrV2ndRin5cvNWdz1+XYm9ormkYv6M7Z7ZL3JSmmVi4Vbj/Le2iP8b9s6EiMsXDI0gUuGxZMcadUg8vYjkprWEJnqW/Mpb2fAJTWSJGFJcFJ1JIYwRxWSufM1ZwYmUVNzWhQVZP9IalRV5ekVK3l9eSFexcSUYaW8OPMKgn63nIuUFI9tn/+3ZDTElJqKt7QUd14ehpgYrcNptiNFdu796jcuH5HIE5cOPGVLa1iQkT+M78a141LYdKSEzzdl8tbqdP77UxqDEmw8NmMAgxLC2i/4duQff5Udnc4A0b19SU0AsozpjaJG4Fj1i9ahCK1IpDSnQVVQ/eA7uDHjCKP+8xGvLKkiKqKYz28eyFuzrzkpoQEI7t6TsAqFkpLArKsJGjYMgKoNGzSOpGXeX3sEm8XIIxf3b3LXsSRJjEyJ4KlZg9n4wFRevnIYLo/CRS/9yp2fbaOwMvBGtoqkprXEDIS8XVpH0TbsRQAodrvGgQitSrTUtJyqdOiWmiqni+s++ZLLXt1GSQXcPF1l3d9uZGRCw4MZonoPBiBz9/r2CrNd6aOiMA8cSPn3/jm/UH6Fk/hwC2ZDywrULUYd5w/qyqJbz+DxmQNZvjefKc/8zOq0wlaOVFsd96/S38T0h/w9oHi1jqTVGYcMxWQ+SPkmUYgRKMSMwqdHUhU66vfvg00bGTrvS1Zs1zOkZwG//uM8/nHmBY1+uk/oOxKAgn2/tUeYmrDNuJjKn3/GneN/rVFT+nRhe2YpN7y3kYKKprWw3L5gKxe/tJovN2fh8fpmwNbrZK4cncTyuyYxJDGMa9/ZwCcbAqdAXNTUtJaY/uCugq9uBFkPXhcobvC6ffe9NVOr64y+L70RDFYwWsEYVM/9IN/j+u4brO36KVGyhqOPtqJkB15TZaclSmpOj9rxhnQfKirkT598z8GsCEJC7Tw3pxfn9bm4yceHRHWlIkjGnn6gDaPUVtiMGRQ8/19KPvqILnffrXU4zXLxkDhUVB5fvJfLXlvDN7eeQajZcMpj9uVWkF1azV2fb+e9tYd5dvYQUrv4ZoiPsBp569oRPPLtbu77agfphXbuObcPOj8fJSWSmtYSPxxSJkDJ4ZrExVDzZQRTCMg1v3xeFygecJRDRS64qsBtB5f9+H21CWvK6C31JENBYAw+4X7Ncw3dN1pr9j/hvq7+PxJvpQvZGHitUJ2XGNJ9WpSOkxV6vAr3LV7MF2tdIJm4ZHwFT553LYYG/pZPpaKLFTXjaBtE2THIVithl1xC6ZdfEX3bbUhG/1mkV5IkZg5NYFhSOBe8sJp//m8Xz10+pNHjLhmWwMVD4rj78+3MfOVXXr1qOGf0jAJ8rTaPXtyf7tFW/rVoN5VOD4/PHNjGr6RtiaSmtZhD4Q+LTv88qgoepy/JcdckOnXuV/pahOpLho7dtxf8bnvNOTzVjV9fZ6on2bFirIihwnMZJV/uRzLqjn/pJJClurc62TfaVZaRZAl0Jzx/7LF8iscnnM93X/Y9bsq3T1HBq/puJZAMsnjzrocKHeZN2T8pHWKemh/27eIfX2ylvCKcbgmFvD7nfHpFxbf4fK74aMwZ2a0YYcdju/QSit97j4qffyb07LO1DqfZkiOtPDqjP3/7dDuzhicwPjXqpH12ZZexPbOMIrtvNvihSeEsvGU8f/14K9e/t5EvbhpbO/pJkiSuG98Ni0HHvV/tYHS3CC4e0vLfIa2JpKajkSQwmH1fRLbuuRWlJiGyn5AE2X2JksvuW8PqxMcuO7gqwGXHkliNs7gMV44d1aWguryobi+q94Qkwts2NTeSQSb27yPQhZrqbFdVlaP3rfY90Em+T8+/D0EnobMa0EWY0R/7irSgizCjsxqQrQYks+6UiY+qqHiKHbhzKlEdvtesepU6r1v1KqAAMjWJmlwn0ZN0ct3k7VjSVs+2eh/r5JMTwBOfbzZRH3U6JEXRNCksqKzgxk+/YWtaKCaLl3/OsnLdiBtP+7yGlCTCNh/C4/Wg1wXm24O5Vy/M/ftT/t1iv0xqAGYMiee/y9L4Zlv2SUmNV1E5/wXf/8U4m5nhyb4JYUPMBubPHc7s19Zyx4JtfHfbBCzG412ol49MZN2hIu7/agcD4210j/bPhYwD87dWqJ8sgynY99VMBiC6CfupigqKWpPsKHUfK2rTHp9w351jp2JFJopL4fcVDEqlu/Z+2AXd67b86CRQQany4K104Sl24CmsxrGvBMXurnsiWUI269BHBxH950FIsoTi8lKxMhNnejnu7EpU5wldb8daoY5dRychyb6kxZfo1CQ83rqvrc1INJwM/b4VTZaQ9DKocPDgZhyvPI852IopKBiTNRhzcDAGowlZr0d37MtgQKc3nLDN4Ls1GtEbjej1Bt/r71RUTSbfU1WVZ35ezms/FeLxWpg0pJSXZl5GsKl15o8K7dEHq2Ml2dn7SErs3yrn7IisE86g9LPPUVX/7IaVJImLh8Qzf9Uhbp/ak7gwS+1zOlliUIKNCKuRd6+ru8iySa/j2cuHcN5/f+GNXw5x25Sedc7575kD+e1oGbd8vJWvbx7X4pFWWhJJjdCqat9I9cBJaUjzVet9bxyy6eRzqV4FQ0IwqsNL8Ni4Jp9TcXjwljrx2t0odjdKlRv3UTv2jbkUvbcLyajDnV+Ft9iBuU8E5kmJGOODMcRZkYMMLWoZUdWaxOaExK2hhK7x55WaxxxPHOvb33vCNU84h+pR6Nf7TIrUHIpzsnDa7TjtlTjslXjd7kZfS310ej16owmdwYDeaEJvMNQkQ3rkY0lQzWOdTu9LkI49ri9hqjlObzCQPGgoti4dbLI0tf0n39uUeYSbP11JfmEU0VHlvHz5JEYl9mz8wGaI7TsMO5C9Z2NgJzWjRlH02uu4DhzA1LN1v4ft5YYJ3ViwMYPbPtnKR38ajUnv+x+5+Ugxv2WVMe+S+mtjekQHM3tEIu+vPcyNE7vXSVysJj0vXzmMGS//ynPL9nPf9L7t8lpak0hqhA7tWAuJ9LukRnV7yX3Ct+Ju2IwezTqnbNYjx+o5sYxScXhQPQpKtQfF4UEfZiJidm+M8a3TBCtJvhYddB1jIPDkWfXPV+J2OfG63Hg9x748KB4PXo8Hr/v4Nq/HjdflxuNy4nHXvfUeu61z3LHzuHG6qho4twflxPPXbO9/5hTOvflv7fwdapiqKEiKh3KHlz055bi9Cm6vitur4Km5PbbNo9TcehXciorXq/gKPofFNzpy5ZhKl5Pbvvwfy38zojcY+cs0mX9MuqFNWhjieg8jDSjZvwvOafXTdxiWIUPAYMC+YYPfJjUhZgOvXDWcK+av474vd3DXtN7syCrj0W93MSQxjNkjEhs89vozuvHBuiMs2Z3HRYPrfiDs2zWUgfE2ftlfyH3T2/pVtL7TSmrmzZvH/fffz+23387zzz+P2+3m//7v/1i8eDGHDh3CZrMxdepUnnjiCeLiTv1J+ssvv+TBBx/k4MGD9OjRg3//+9/MnDmzzj6vvPIKTz/9NDk5OfTv35/nn3+eCRMmnM5LEDo4xen11akY6n4qPnGAmHX46X+Kl816Ii7vfdrn8XcGowmD0dT4ju3k66cepbqivMXHq6qK1+PB43TidjnwOJ14XC7cTicel7PurdOJ2+Ws2ef4/WP7VFeUU1FUSGVxERavlwJdAtP/2/RZtnWyhE6WcHkUbBYDM4Y2Xoz5weZ1PLboIM7qEIb0KuL12TOJCQ5r8fejMfogK6VhehyH09vsGh2BHBSEZeBAqjZsJOKqq7QOp8WGJ4fz1KxB3PnZNr7a6hu1NjgxjFevHnbKodm+lbxDWbIr96SkBqB3bAj/25ZNid1FuNV/RojBaSQ1GzduZP78+QwaNKh2W1VVFVu2bOHBBx9k8ODBlJSUcMcdd3DRRRexadOmBs+1du1aLr/8cv71r38xc+ZMvv76a2bPns3q1asZPXo0AJ9++il33HEHr7zyCuPHj+f1119n+vTp7N69m6SkpJa+DKGDU50eJJP+pE+lsqmmyUMF9J2tnqPzMAdZyTmYxpbvv8VVZT+eaPwu4XDXJCv1JSpqU6ZIAGSdHoPJ5KsTMvmSuxNvQ6O6EN+7HyGR0WC1MS66G/8IsqKXJYx6Gb0sYdDJNV8S+mO3su85WZZwexV6PvA93kZqrA4W5vGnBd9zKCuakFAXz12exHl9LmqNb2mj7LE2pKzcdrmWloKGDaV88fdah3HaZgyNZ1S3CPblVpAUGUSPJhb4TuwZzeebs+qtK/rb2b1YuPUor6066HddUC1KaiorK7nqqqt44403eOyxx2q322w2li5dWmffF198kVGjRpGRkdFg8vH8889z9tlnc9999wFw33338fPPP/P888/zySefAPDss8/yxz/+kRtuuKH2mB9//JFXX32VefPmteRlCH5AcXpPqqdRXF4c+0pABevYrn5Z6Cc0TVhsHLt/WcGqD9/CZA2uSTpMvtuahMNgNhMUavPV8phMdff53b51ExZz7T56oxGdvv164xtKaTxeL/ct/pYv1nkAK5eNd/L49GswtGNsSnwM1p372+16WjGmpODOzcVbWoouLEzrcE5LXJilTrFwU/SLC6VgpZPyag+2oLpdoVHBJuaOS+GDtUe4eVIqNkvz5zzSSov+Um655RbOP/98pk6dWiepqU9ZWRmSJBF2il+atWvX8re/1e0znzZtGs8//zwALpeLzZs3c++999bZ55xzzmHNmjUNntfpdOJ0Hp8Ft7y85c3YgjZUpxfVo1C25DDu3CrceXa8xQ7f4BOjDtu0FK1DFNrQmEvnMPyCGRhM5oBPXr/f+xv/+GorFeVRdE8oZv4V00mN7NrucZi7dSf0593YnZVYWzBS0l8ET5qEbDaT/8wzxD76aMD/fv2e1eh7+692e7FxctJy3fgU3volnU82ZHDTmc2rW9RSs5OaBQsWsGXLFjZu3Njovg6Hg3vvvZcrr7yS0NDQBvfLzc0l5ndLwcfExJCb62sCLSwsxOv1nnKf+sybN49HHnmk0TiFDkwvo1S6sW/IxRBrxdI3EkNsEIZYK/ouQchG/xtyKDSdJEkYzc37BNqRHXvbVNXjbTUFleXc+OlCtqaFYbLIPDIrjGtHnK9NgEB4z/7oPYs4cnAL/fpN1CyOtqaPiqLLPfeQ+89/otir6PrEPGQ/mmH4dJlq6hSdnvpniu8SYmbm0Hje+TWd68d3w+gn3fzNSmoyMzO5/fbbWbJkCWbzyUvXn8jtdjNnzhwUReGVV15p9Ny/z5Lr6+dryj4nuu+++7jzzjtrH5eXl5OY2HBFuNDx2KalEDIxAZ3Vf5o/BaEpFEXh6vd+ZM0+BaRQJg+x89LM2VhN2hZqx/cbQR6QuzewkxqA8Mtno7PZyP7739FFRhL7wP1ah9Rujg0Bd3oarjm7YUI3Pt2Uybfbs7l0eEJ7hXZampXUbN68mfz8fIYPH167zev1smrVKl566SWcTic6nQ63283s2bNJT09n+fLlp2ylAYiNjT2pxSU/P7+2ZSYqKgqdTnfKfepjMpkwafwPQjg9kiyJhEYIGMdaaB767lceWuymuioCgM9vHsTIxO5ahlYrPKU32TKUH9indSjtIvTcaXjy88l7/HGCJ04guJOMqDUfa6lxN5zU9IwJ4aw+XXjjl0NcMizeL7romtWeNGXKFHbs2MG2bdtqv0aMGMFVV13Ftm3b6iQ0aWlpLFu2jMjIxqf6Hzt27EkFxkuWLGHcuHEAGI1Ghg8fftI+S5curd1HEASho5NkFWPkcoJDComP9HDjVAOHHp/eYRIaAMlgoDTSjOfIEa1DaTfh11yNdcIEsu+7H09xsdbhtItjLTWOBrqfjvnThO7sza1gVVphe4R12prVUhMSEsKAAQPqbLNarURGRjJgwAA8Hg+zZs1iy5YtLFq0CK/XW9u6EhERgbGmv3Lu3LnEx8fXjlq6/fbbmThxIk8++SQXX3wx//vf/1i2bBmrV6+uvc6dd97JNddcw4gRIxg7dizz588nIyODm2666bS+AYIgCO3J1GUJD4z/FzNSZ2gdSoMccREYjvrHm1hrkCSJuMf/zaGLZ5D78CMkvPBfrUNqcyZ94y01AGO6R9AlxMS1b2/g8BPa1Xo1VatW/mRlZfHNN9+QlZXFkCFD6Nq1a+3XiaOUMjIyyMnJqX08btw4FixYwDvvvMOgQYN49913+fTTT2vnqAG4/PLLef7553n00UcZMmQIq1atYvHixSQnJ7fmSxAEQej05KR4QvPtdQqaA50+OpqYe++hYskSqrdv1zqcNndseYSGCoWPkSSpdnbiA/mVbR7X6ZLUTvRbW15ejs1mo6ysrNE6H0EQhNbmVtwM+2AY/+rgLTXrX3kU64ufELV2KbFh/lEg2hpUr5f0GTPRd40laf58rcNpU+UON4MeXsLLVw7j/EGnnjrA4fYy+vGfmDMykfvO02Yyvqa+f/vHGC1BEASh3cSNmoSkwra/XIOzquN/Om8tkk5HxHXXYV/1C66MDK3DaVO13U+NtNSAr1Vn5tB4vtxyFLe3aTN0a0UkNYIgCO1M6hDLmjYsccREih6+ga47cllx5dlUVZRoHVK7CT1vOjqbjZIFn2odSpsy6mQk6dRDuk80e0QihZVOVuzNb+PITo9IagRBEISTTJxzF56n7iXmYCm/zjkXe2mB1iG1C9lsxnbppZR9+SWKw6F1OG3m2PDsphag9Ivzrd792aZMlEbWLtOSSGoEQRDaS8d9L6jX8POuRXn+QcKzylk/+zwqCnMaPygAhM+5HG9ZWUAseHkqza2onT0ykWV78rl8/tq2CagViKRGEAShnfnDJGbHDJ9yJfJL/yK4wM66ay7G4bRrHVKbMyYlYZ0wgZKaBZUDWXN+FWcOjceol9l4uIQSu6vtgjoNIqkRBEEQTmnYhFkYn36QuMMV/PT4rVqH0y7Cr7gCx44dVO/YoXUoHUawSc+qv08G4JcDHXMeI5HUCIIgtBPV3/qfTjBk6hWkXzSUpM/XcnB1YHfLAASfORFDXBwlHwdua41OlvA0sz4m1mamT2wIq/Z3zBorkdQIgiAITTL5n69SFK7j8KMPah1Km5N0OsLmzKF88WI8JYE5+suok3E3cfTTiSb2imbV/oIOOTmjSGoEQRDaWUcf0t0Qa5CN8u5d8Ppn+M0WNutSVI+HiiVLG9/ZDxn1Mq4WzDszsWc0+RVO9uZWtEFUp0ckNYIgCEKT2bLLSTxiZ9u2H7UOpc3pIyIw9+1L9ZbNWofSJgw6GVcLWmpGdgvHYtB1yC4okdQIgiC0E3+uqTkm7uGHAShYEZitF79nGTKE6t8Cs1jYpG9ZUmPS6xiREs66Q0VtENXpEUmNIAiC0GQRPfoDkPD6dyhKx54yvzXoY7rgLS7WOow2YdTLLV72YEz3SDYeLsHTwZZNEEmNIAiCRspd5WzM3ciHuz9ka/5WrcNpkoiuKew5MxmA3EOB2YJxIl2oDW95OWoAJnBGndzkZRJ+b0z3SCqdHnZll7dyVKdHr3UAgiAInc39q+/npa0vkW3Prt02MWEiL095WcOomkaSJMbd/xwlP19C+rqlxKUO1jqkNqWz2UBVUSor0Z1idWh/1NJCYYBBCTYsBh3rDhUxODGsdQM7DaKlRhAEoZ0YZANjuo5hRMwIpqVM44kJT7Dw4oUMjBpIuClc6/CaLDa5LyWhMmXbt2gdSpuTrUEAKFVVGkfS+owtrKkBX5FxR6yrES01giAI7USWZN44542Ttpe7ygk3+09SA1DSLRL9vnStw2hzkt73Nql6PBpH0voMOqnFNTXg64J6deVBPF4Fva5jtJF0jCgEQRA6MQkJp9epdRjNIvfsRlhWWYecgK011SY1brfGkbQ+o17X4pYagOHJ4VQ6PRws6DjrgYmWGkEQBI31jejLnqI9WofRLOH9hxL6xQYyj+wkKWWg1uG0nZqkhgBsqTHqZCqdHjYfKWZ7Zhm/ZZXy29EyzHodi2+f0Ojx/eJ8NUZ7csrpHRvS1uE2iWipEQRB0Fi/yH7sK9mHV/FqHUqTpQw9E4CMrT9rHEnbknQ6ANRmrpHkD8wGmV/SCrn01bU88cNeDhdVERFkZHdOeZNa4ELNBuLDLOzO6TgjoERLjSAIgsb6Rfaj2lNNelk6qeGpWofTJF16DiLXAOV7dsJMraMRWuKvZ6VyRmoUA+Jt9IoJwaiX+WpLFpuOlODyKpj0ukbPMSjBxvbM0rYPtolEUiMIgqAxk94EQJXHf0bYVFcU4zbIeLOyG99Z6JD6xIbSJ7buMHWpmet6DUoI46XlaSiKiixrvyiY6H4SBEHQ2JrsNYQYQugX2U/rUJok++g+Nl18NrJXoftVN2gdTps6NpRbNhk1jqR9HBsMJTcxuxmUYMPu8nKosLINo2o6kdQIgiBobM3RNYyJG4Ne7viN56UlOez6wxUEVbgxv/U8/cdfpHVIbcqVmQmyjCEuTutQ2oVSU0uja2JSMyDeBsBvWWVtFlNziKRGEARBQ06vkx2FOxgeM1zrUBrlcNr55boZROQ7CH75afqPmKZ1SG3OnZGJITYWydg5WmqOFQg3tRvKZjHQLcoqkhpBEAQBjLKR5NBkNuRs0DqUU1IUhe/vuIxu+8oxzXuAvmPP0zqkduHKzMSQmKh1GO1GUX0JjdSM4ppBCTZ+yyptu6CaQSQ1giAIGpIkiXO7ncuKzBVUuTtuofD3T91CnxXplN46mwHnXaV1OO3GnZGBManzJDVeRW1yPc0xvWJCSC/sGBPwiaRGEARBQ7n2XD7a8xHj48dj0Vu0Dqdevyx4lpR3V5Jx4TDOuPkRrcNpN6rHgzM9HWNKN61DaTeqqtLcQUzxYRZKqtzYndpPUCiSGkEQBA19sPsDJCSemPBEs5r828vuNYsI+fcbZAyN5ewn3tc6nHbl3L8ftboay5DAXon8RIra9JFPx8SH+5Lxo6XVbRFSs4ikRhAEQUN2tx2byUaoMbTxndtZzqGdlN9+D4WxQZw5/ytkXeOTsQWSimXLwGDA3L+/1qG0G0VtfvdTfFhNUlMikhpBEIRO7aykszhSfoQDpQe0DuUkW194BFuFQtITTxEU4l+riJ+uqi1bKXx9PpHXX49sNmsdTrsJMupweLx8sTmryceEmH1TEZRUudoqrCYTSY0gCIKGxnQdQ7AhmGVHlmkdykkG3nwvFUES6Q8/gNup/afw9lJeUMzWP99KSVIq0X+9Retw2tXFQ+K5bHgCd3++nQe+3oHT0/h6ZCFmA/3jQvlhZ247RHhqIqkRBEHQkFFnpEdYDzIrMrUO5SSJvYajm3cfcQfLWHLXlVqH0y5UVWXVTXdhrLZzV69Z7MrvuCPS2oLZoOOpWYN54pKBfL4pi9mvr2tSrcyEntHsydV+YUuR1AiCIGgspzKHCHOE1mHUa/i0a8j58/l0X7aX9V++onU4bW75M2/QY9c6iv9yN6EpSdz/9Q68AbhCd2PmjEpiwZ/HsD2zlPu/2tHo/jaLgQqHGP0kCILQ6XUL68Z7u98jq6LpdQztqcdZFwPgrm7aXCSqx0N1vvZdEc11YMNvRL7zEntHnMW0W67i8UsGsvNoGc8v2691aJr4Na0QgL9M6tHovqEWPeXVbhSNE0CR1AiCIGjs8TMeB+Cun+/SOJL67f1kPlUmGH7Jn096zllWwr6VC1n13/tYctMMVp49kh1DBnJo4mT2rv1eg2hbpqSonCN33ElxSCRTX3oCgOHJ4dw9rTcvLj/QIepF2tNXW7J4Zul+bpzYnTHdIxvdP9RsQFHB7tK2tabjr54mCIIQ4LoEdSElNIXdRbu1DuUkiqIQunIbOSNTSMjLZMemlZTu3IaSdpDgI4VEFrkBCJchJ8ZAcXIUJZMHE//FGgq+eJc+Y6dr/ApOzW6vZsl/3iRq4cdEuewYXnuHkLCQ2uf/cmYPdh4t45aPt/DQBf2YOza5Q84n1FrcXoV3fk3n8cV7AfjDuJQmHWezGAAoq3YTYja0VXiNEkmNIAhCB5AUmsTh8sM8u+lZ/jr0rxh1HWMBxd1rviWq2EPU6sMUT59FCCCZoTAhhKLh3bH37UuXwaPpPngCA63HP9EvyriAsDW7UBQFWe54nQIuh5Ofnn8H6+cf0MteQuaQ8aQ+cDddB/aus58kSbwwZyiPL97LP7/ZxT+/2cULVwzlosGBt2r3ir35/Ou73RwutPPHM7pxz7l9MOqb9rM7MalJ0HD0v0hqBEEQOoAXJr/AO7ve4ZVtr/DL0V+YN2EefSL6aB0WEV27sW10IlJCV0IGDSVx2AR69RiCTj71RHwxF1xC8Mqn2bPmW/qfcXE7Rds0mxZ8S/UzT5FSUcjBAWNIvu8uzh0+oMH99TqZhy7sx9u/pgOwN6c8YJKa9EI7CzZksDWjlA2HixnbPZKXrhhGv7jmTQYZekJSoyVJPbbOeCdQXl6OzWajrKyM0NCON3unIAjCvuJ93L/6fg6VHeKWIbfwh/5/QC/73+dPt8vB1jHDKJg8kPOf+VTrcADIz8hh/V0PkLpjLWlJ/Ul96H76nTHs/9u77/CoqvSB49/pM5n03kNooQUIIL0KgoiCDQURBRVFRUHF9ltdRVB2FRsoKiyKKIKuoqC4CqyABaT3GiAkISG9t5lk5v7+iESzBFKYZFLez/PMA7lz7znv4fBwX+49pcbXbzudwR1LdwCw4bHBtA9wq+aKximjwMLSX87w/aHzJGUXY1egta+Zp0d3YGSngDq9XssqtNJj7kbev7MH13YJcnjMNb1/X9Ezwfnz56NSqZg1a1bFsTVr1jBq1Ch8fX1RqVTs37+/2nKGDh2KSqW66DNmzJiKc1588cWLvg8MDLyS8IUQotGJ8o5i1ZhVTOk8hUX7FjHlhykk5CU4O6xa0+mNpF3VGo9tR7Hb7c4Oh5+WrCZz5NUEnDxIwvSnGPOfL2qV0AD0b+PLL08NI8LHhZFv/swDn+ymKT0XOJ6Sx+x/H2DgP3/io1/P4mM28LcxnTg+91p+mj2UUZ0D6zxeyP2PVYXzipvoQOFdu3axZMkSunbtWul4YWEhAwYMYPz48UybNq1GZa1Zswar9c/llTMzM+nWrRvjx4+vdF7nzp3ZtOnPVTc1LWwfEiFEy6DX6JnZYyaDQwfzt1//xq3f3srsXrMZ3358kxqkGjjmRsxbX2fj2H54pBSQde1VXDdvuVNiKfjuOwAivvma3pFhdS4nzNuFH2cNpsPzP/DjkVRuXLyN6YNbM7JzIJrabm9dT0pKbazdn8S+hBx+ic0o33lbreLcH3szPXp1W6YOiMTL7LhxW1qNGleD1umvn+qU1BQUFDBp0iSWLl3KvHnzKn03efJkAM6ePVvj8ry9Ky86tXr1alxcXC5KarRarTydEUK0GDH+MXx5w5cs2L2Aub/P5afEn/jHwH/gafR0dmg10m3kJP774YfYtWoKPfSYft3vtFjCZz8G99/JsfU/ETDj7isqy6jTEDf/OracTOeDrad5cOVeWvm4cN+g1tzaMxSjrmH/w22zK3x7IBmTXsO6A8lsPJqKtaz86djNPULwdtGTlFNMh0A3pg9pQ69W9bPQo4dJ1zSTmocffpgxY8YwYsSIi5IaR1i2bBkTJkzAbDZXOh4bG0twcDAGg4E+ffrwyiuv0Lp160uWY7FYsFgsFT/n5Tl/CWchhKgNF50Lf+/3d4aFDeOZX57htd2v8fLAl50dVo3ojCau/WYbAP9972/4v72G/OxU3LwCGjyW7oN7ssErBNu6tXCFSQ2Uz4oaFuXPsCh/9ifmsOTn0zz3zWGe++YwN/cI4ZWbous9uVEUhR+PpPDGxpOcTC0AIMLHhdkj29M52IMOgW74uBrqNYa/cjfpyCtpYknN6tWr2bt3L7t27aqPeNi5cyeHDx9m2bJllY736dOHFStW0L59e1JTU5k3bx79+/fnyJEj+PhUvTDQ/PnzmTNnTr3EKYQQDWlQ6CAe6/kYc7bPYXz78XT37+7skGolot81lL69hhO/fkevG+5t8Ppz0rPwz03FUpTr8Gnm3cM8WTypJwfP5TD2nd9YszeJn46ncXWUP93DPRkW5U+Ipwm1A19P7YnPYs63Rzl4LpeBbX15aGhbgj1N9Aj3RKtxzhR6g1aNpdS546dqldQkJiYyc+ZMNmzYgLGetmJftmwZXbp0oXfv3pWOjx795wJO0dHR9OvXjzZt2vDxxx/z+OOPV1nWs88+W+m7vLw8wsLq/i5VCCGc6aa2N/HFiS/4x85/8NmYz1CrGt/6L5cSGT2QvSYVmTt/BSckNVv/9g/CVWq833u/3tbN6RrqSdz86zidXsC/95xj++lM1h1I5u9rj2DUqYn0daWtvytt/MxE+Ljg72bEw6TDbNDiatDi5aKrUULy+a4Enl1ziOgQD1ZN60u/NtWv+NsQtGoVZU7eJqFWSc2ePXtIS0ujZ8+eFcdsNhs///wz77zzDhaL5YoG7xYVFbF69Wpeeumlas81m81ER0cTGxt7yXMMBgMGQ8M9ehNCiPqkUWt4uvfTTPlhCj+e/ZHRkY17td6/0mi0pLX2QnO44fdRstvthG3bQGKf4Yzt37P6C66ASqWirb8bz47uCEB6voUjybmcTi/kdHoBp9IK2H46g4wCa5XXuxu1eJv1jO8VxkND21w0MPz1DSdY9NMp7ugTzktjOzvtqUxVtBoVZU6e6VarpGb48OEcOlR5t86pU6fSoUMHnn766SuejfTFF19gsVi48847qz3XYrFw7NgxBg0adEV1CiFEU9IzoCeDQwezaN8iRoSPQKdx3pL0tdalPf7f7MBus6FuwNmr/3nuNVqXWfAZMbTB6rzAz83A0Ch/hlZeqJhiq430fAs5xVaKrDbyikvJKSolu8jK6fQCXvvxBMfO5/Hard0w6cv/rDafSGPRT6d4clRUlQmPsxm0mqb1+snNzY0uXSqvumg2m/Hx8ak4npWVRUJCAsnJyQCcOHECgMDAwIqZS3fddRchISHMnz+/UlnLli3jxhtvrHKMzOzZs7nhhhsIDw8nLS2NefPmkZeXx913X/mALyGEaEpm9pjJretu5avYr5jQYYKzw6kxn6sGYPr8d+IP/kpkzJAGq1f728+cjozm+jvGNlid1THpNYT7uBCOS5XfD4vy58GVeymy2vhwylUcSMxh6kflY1kfHNL4EhqbXeH3M5n0qcHml/XJ4c+t1q1bR0xMTMXCeRMmTCAmJob333+/4pyEhATOnz9f6bqTJ0/y66+/cu+9Vb9rPXfuHBMnTiQqKoqbb74ZvV7P77//TkREhKObIIQQjVp7r/Zc3/p63j/wPkWlRc4Op8aiBozBpoL4bRsarM6cjGwC0xOwx/RqsDodYXR0ENMGRfLT8TQOJ+XyyKp9BHsYmX9ztEMHHDvK/sRsLGV2jDXcK6q+yDYJQgjRBCUVJHH919fzYLcHub/r/c4Op8Y2Xd2d0tYhjP7X+gap78u7H6Pd7v/i8/U6Qtu3apA6HSWvpJTRb/1CUk4xXi46vnl4ABE+5uovdIJfYtOZvGwnvz49jFCvqp8+XYkG2SZBCCGEc4S4hnB71O18dPgjckpynB1OjRV2CMX1xLkGqWvrym/pvOMHMiZNa3IJDYC7UcdHU6/irn4RfHpfn0ab0ACU/DGWpqEXHvxfktQIIUQTNS16GnbFztJDS50dSo25xMTgm24lPy2pXutJO5eKbsE8zkZGM+zph+q1rvrUPsCNl8Z1oXOwh7NDuSxLmQ0oX6vGmSSpEUKIJsrH5MOUzlNYdXwV5wvOV39BIxDRfyQAsb/W3+snu93O9hlPobOVEfPOggadadVSXZj1ZNDKkxohhBB1dFfnu3DTu/Hu/nedHUqNtO7QjxxXFRm7fquX8u12O98//zrtj++keMZsAtuE10s9ojJLmR2VCnQa5w5ilqRGCCGaMLPOzP1d7+fbM99yKvuUs8OpllajJb2NN+rDl1449Upsem0Jbb76kFPR/Rl8/8R6qUNczFJmw6jVOH2quSQ1QgjRxN3W/jaCzEEs3LfQ2aHUiNKlPb5ns7Fbq15V90qUbd0MwNAP3nB42eLSSkrtGHTOTymcH4EQQogrotPomBEzg82Jm9mftt/Z4VTLr/cgDKWQsO8Xh5ZblFdAcPwxYm+5Bzfvxj2wtrnJKrTgYXL+6taS1AghRDNwXeR1RHlFMX3TdJ7Y8gT/ifsPdsW5S9ZfSse+11GqgYTtGx1a7v51mzDYSmk/runsidWY2e0K6w4ks+loKvZqNqo8lVZAGz/XBors0mq1TYIQQojGSa1S8+7wd/n61Nf8++S/2RC/gYPpB5nUcRKhbqHODq8SL48AdgQbUO8/UKvr0he9g+XUKYJemoPG4+InMRkbf8Lq5sfgXp0cFWqLUmqzszc+m8nLdtI11IPd8dkV33UKcmfRHTFVJi52u8KJlHzGdA1qyHCrJEmNEEI0EwHmAKZ3m84dHe/g8S2Ps/r4aj499imRHpEMDBnIwJCB9A7sjVbt/H/6CzuEEbQ7vnbX/PYbxfv3U3LsGKHvLMLYvn3Fd3a7HZ9Du0jp3h9ofNsINCY2u8IHP59mb3wOHiYdBp2az3YkVDpnd3w2s0a0IybcC5NOw/99fYiJS37nh1mD8TbrK5276VgqybkljOoc2JDNqJLz/2YLIYRwKHe9O/8a+S/yrfnsOL+DX5J+4ce4H/nk6Cc83vNxpnaZ6uwQMffoidfGU+QlxeMeUrM9/Mqys3AbfS3WM3GcnTCR4Fdexv3aawFI2XsI76IcXiOU2X/7HleDlmAPE1880A8PF+eP9XA2RVFIzCpmzb5zfL4rkfO5JQB0C/PkbEYhAOHeLrxzRwzt/N0qdga/4LP7+nDNmz8z/dM9vHl7d0I8TRXfrdmbRJcQd3q18m64Bl2CJDVCCNFMuendGBExghERI1AUhaFfDMViszg7LABa9R+Fwuec/vV7Ym5/sEbX2DKzMHWJJvjllzn/3PMkzXqMkmlH8Js1C+1vW1Dc3Lj/kVvJK1U4ej6PFdvjSS+wtLikpsxm54vd54gKdMOk07DxaCpr9ydx5o/kZWLvMEZ2DqR9gBshnibyS0r5/UwWw6L80GqqHmrr727k9fHdePizvUxYsp3vZgzCw0VHSamNrSfTeXR4u4Zs4iVJUiOEEC2ASqWi1F6KQWNwdigAtGvXm988VFh3b4MaJDV2qxV7QQEaH2/ULi4Ev74AY5cupC1YQMnRY5QcP47n8OFcFxMGwO9nMlmxPR5tI9zR2tHS8ktYvPk0ZzIKScgsJDG7GNtfBvaa9Rqu7RLE2O7BXN81iLb+bpWudzPquKZTQLX1jOgUwMPD2vLGxpOU2csHof98Mp3iUhsjO1d/fUOQpEYIIVoIq82KXqOv/sQGoFFryAn3whh7pkbnW44fB0AXUH7zVKlU+NwzFWOHKBLuuRcAY8cOFedfuKlrmnlSsyc+m4dW7iE1z8LAtr5c0ymACB8zbkYtwZ4mymwKMeGeDttoMj6ziI5B7vi4lifHW06m09rP3ChmPoEkNUII0SIoitKokhoAXVAgmgOXX1lYsVrJ/vwLMt55B33r1rhcdVWl7839+xP2wfskPjAdj3HjKo6XNfOk5nBSLq/+eIKfT6YTFeDG3HFdGFnPA3UVRWHb6QzGRP85y+n4+Ty6h3rWa721IUmNEEK0AGX2MhQUp7x+UhSFf5/8NyatiTGtx6BWlY/b0PkH4JZ3FEVRLlpeX7Hbyfv+P6S/9Ralycl43HwT/k88gUp78W3LdcgQOh4/VumY7Y/XI83t9VN+SSkrtsfz2o8nAHjnjhiu6xKEugHaeSajkPO5JQxo6wuU92tsagHXdHL+rKcLJKkRQogWIL80HwC9uuGf1BzOOMzc3+cCsO70OuYOmEugORBXuw63YsjNS8PT488xGQW//Uba669jOXoM1+HDCXv/PQxt29aqTtsf6w5efsm4xk1RyqNXqVQoisJXe5OY//0x8kvKmNQnnMevaV/xGqghbDuVgVatondk+SynlLwS8i1ltPNvHK+eQFYUFkKIFmHFkRWYtCa6+3dvsDqzSrK4bs113PH9Hfi7+LNgyAJOZp/khq9vIPrjaBbaNwGQE//nK6iCX38j8d77UOsNRHy2krB336l1QgN/PqF5eOVexzSmgR08l0OfV/7LF7sTAXh+7WFm//sA/dv6suXJobx8U3SDJjQAv53KJCbcE7Oh/HnIydQCANoHuF3usgYlSY0QQrQAhzMPMzBkIIHmhntVYNAYSMwvvymvvG4lo1qNYmTEyIpxPWcCyxMPj7jMP69pHQmA15134tKjR53rHtjOl3b+ruSVlNa5jIZWUmrjVFoB3+xLYuKS30nLt7D7bDYZBRY+/T2Bv13XkUUTYwj+yxoxDcVmLx9P07+Nb8Wxw0m5mPUaQr0aPp5LkddPQgjRAqQVpdHOs2HXEjHrzMzsMZN3979Lmb0MgNSiVBQUrgq8ilPZp9BFmMhcvBilsBCPsWMpOVY+NkbtcmU3Sp1GzYC2vmw/nVn9yQ3EbldIzS8hPrOIhMwizmYWkpBVRGJ2MUnZxWQU/LmG0JD2fui1as5mFnIytfzV4dAoP2eFzpHkXPJKyirG0wBsPJrKoHZ+DTKep6YkqRFCiBbAqDFitVkbvN5JHSex6tgqFu5byKuDX2Vo2FA2J27G1+TLrpRdHHx0HB3WHSL15ZdJfal83I3riOG4DhvW4LFeqbT8Ekqsds5kFLA3IYdiaxk39wjF26znrU2xfL3vHCWl5YN9VCoIdDcS7u1Ce39Xro7yJ9TLRKiXiRAvEyGeJt7aFMuSn88w+4sDhHu7EOFjdlrbfjuViYteQ/cwTwBS80rYn5jDm7d3c1pMVZGkRgghWgB/F39icy4/fbo+mLQmHur+EC9uf5G7O9/N6MjRvLDtBbr5dcOu2Hnh7HLoD95dVKxmOhqrDb9HH7loNlRduOg1FFjKrrwRNfCvX84wb/2fM7B8zHoyC60s/SUOg1aNSa/hwSFt6RLiToSPC6FeLtWuHXNrz1CSc4rJLLTy0rjO6LXOGzGy7XQGvSO9K2LYcDQVjVrF1VGNY9G9CySpEUKIFmBsm7E8sfUJDqYfpKtf1wate1zbcXxy9BPe3P0m/9fn/wA4l3+O5/s+j16t59sz35LlruLjjsU80/sZh9Xb2s+VpJxiiqxluOjr73ZXZrNXJDRL7+pFKx8X2vq7MvCfm0nKKeb+wa2ZNrg17sbabdcQ5u3Ca+Od/ySkpNTGzrgsZo+Mqji24UgKfVt7N7otKGSgsBBCtAAjIkYQ6RHJiqMrGrxurVrLzB4z2ZGyg6SCJHr49+D3878zcPVAvj3zLW092zKrxyxWHlvJqexTDqu3jV/565oz6YUOK7MqJ/4Y8zK2WzDXdAqgXYAbKpWKzx/oy+7nRvDEyKhaJzSNyd6EbCxl9orxNLnFpWw/ncnIRrQ+zQWS1AghRAugVqnpG9SXM7k125bAUUptpayJXcOC3QsASCpIYnzUeE7l/Jm8nMo5VTGQ+GjWUYfV3eaP9VNOpxc4rMyq/OuXOIw6Nf+4JbrS8VAvF3wbeNp1fdh2KhNvs54OgeVTt7ecSKPMrtRov6iGJq+fhBCihfAyepFdkt1g9a09tZZF+xaRWpTKiPARvDr4VTr7dialMKXinP2T93P7d7fzzv53CHAJYHSr0Q6r392ow9/NwOm0+ktqCi1l/HA4hYeGtq3XV1zO9NvpDPq18amY5bThSCpdQz2cMrW8Os2zB4QQQlzE2+BNTklOldsS1Ielh5Zi0pr4Ztw3tPFsU3E80BzI7VG3Y9QY0ag1vDrkVRbuXUjvwN7oNI59TdPW35XT9fj6aeWOeMrsdm7uEVJvdThTXkkpBxJzGN+zfPfzklIbW06k8eDQNtVc6RyS1AghRAvhZfSiTCkjz5qHh8Gj3usbGTGS1cdXE+J68Q3/ub7PVfy+tUdr3hr2Vr3EoFGrUOpps4Tc4lI+2HqGW3qEEurlUi91ONuOM1nYFRj4x3iabaczKLTa6n3zzLqSMTVCCNFCeBm9gPLtCxrCuLbjyC/NZ3Pi5gap73/9cPg8209nEhXg7vCyFUXhxXVHsJTZmTWivcPLbyx+O5VBqJeJcJ/ypG3DkVQifc2Nar+nv5KkRgghWghvY/lGhA01ribCPYIY/xjWnlp7xWWVpqVRmpRUq2te/v4YQ9r78fAwx78q+eT3eL7el8S8G7sQ6GF0ePmNxW+nMhjwx9YINrvCpmOpjOwU0CCvL+tCkhohhGghLjypacjBwuPajGNb8rZKg4Nrqywzk/gJEzl9/Q3k/fBjja7JKLCQmFXMjTEhaDWOu9UpisJPx1N56dujTOnfihtjmudYGoC0vBJi0woY0K48qdmbkE1GgZWRnRvfrKcLJKkRQogWwkPvgQoVWZaGef0EMKrVKIxaI1/Hfl2n64v37yfulluxW624DhxI0qxZZK2ofq2dA4k5ABXL+tdUfkkpy36NY+WOeJJzilEUhYTMItbuT+KNDSe49f3t3LN8N31b+/C3MR3r0KKmY9sf+2b1b+MDlC+45+tqICbMy5lhXZYMFBZCiBZCo9bgafBs0Cc1rnpXxrQew5cnv+S+rvehU9dsdpOiKGR/9hmp//gnps6dCXn7LbT+/qS//jqpr8ynLCMTv8dmXfI1yIHEHHzM+svuIF1stXE2s5CU3BK+O3iew0m5FQvpVcXPzUCkr5lld/fi6g7+jfYVjKP8diqDDoFu+LoaUBSFDUdTuaZTQKPawPJ/SVIjhBAtSEOvVQMwIWoCX578ks0JmxnZamS159uLijj/9xfI++47vO6aTMDs2aj0egD8Z89G4+1D2quvUpaVSdCLL6LSVr6VlZTa2HoynW5hnpUSj9PpBfz3WCq7z2aTmlfCgXO5Fd8ZdWpu6RHKpL7h9IzwItzbhUdW7eNwUh7/uDmanhFeeJn1DvoTaTqSc4qJTc0nt7iU+MwiXhzbeF89gSQ1QgjRongZvRps9tMFUd5RxPjH8PmJz6tNaixxcSQ9+ijWpGSCX1+Ax5gxF53jc89UNN5enP/bc9iycwh5fQFqY/lg3VNpBcz4bC9xGYW8f2f5rKTUvBJmrt7H72eyMGjV9Aj3QqVSMaV/K66LDqLAUsqgdn7o/mfszfKpvR30J9A0PXd9Jw4l5XLr+9vJLymljZ+54lVUYyVJjRBCtCCeBk/yrHkNXu+EqAk8/cvTnM45XWkhvr/K+3ED5//v/9AGBBD5xecY2ra9ZHmeN96I1suLczNnkXDffYQtXszaU3k8981hgjyMrJ0xgA6B7uQWl3Lz4m3YFYV37+jB1R38Mekvvzu2KOdh0rF8am+e/uogQ9r7MalvOAZt4/6zk4HCQgjRgrjp3ci3XnrcSH0ZETECb6M3n5/4/KLvlNJSUv/5KkkzZ2IeNIhWX3xx2YTmAtchQwj/6EMssafYOe425q74hWu7BLJuxkA6BJavTfPu5lNkF1n58sH+jOkaJAlNLQV6GPn4nt7cMzCy0Sc0IEmNEEK0KM5KavQaPbe0u4V1p9dRWPrntgWlqWnET51K1iefEPDsM4S8+QYaV3ONy3WJiaHVp59AXi7/t+sT3ritO2ZD+UuI7EIrK7af5Z4BkYQ0wn2KhONdUVIzf/58VCoVs2bNqji2Zs0aRo0aha+vLyqViv3791dbzvLly1GpVBd9SkpKKp23ePFiIiMjMRqN9OzZk19++eVKwhdCiBbHTeecpAbgtqjbKC4r5rvT3wFQ+PsO4m65hdKERCJWfIz33XfXaUaRoV078u+4jy6ZcawffRv7Ondjz3ebWbkjHkWBKQNaObglorGq85iaXbt2sWTJErp27VrpeGFhIQMGDGD8+PFMmzatxuW5u7tz4sSJSseMxj9Xafz888+ZNWsWixcvZsCAAXzwwQeMHj2ao0ePEh4eXtdmCCFEi+KsJzVQvpHl0NChfH5sFcO35pD+9kJc+vQmZMECtD5XNgC191038dvGH9CUFGO0WYl7ZT5vDXyYnm388XU1VDrXXlxM9merAND6+qDx9UXr64vWxweNlxcqTeN/zSKqVqekpqCggEmTJrF06VLmzZtX6bvJkycDcPbs2VqVqVKpCAy89AZZb7zxBvfeey/33XcfAG+99RY//vgj7733HvPnz69dA4QQooVy07tRYivBarOi1zT8FOVbAkeR8MZs0k+dwOfB6fjNmOGQJMLTz5sxP/wbgMObdxD+8L28yyH6TH6q0nllGRkkPvQwlmPHUOn12Av/ZwdvtRqNtzdafz/MffriPmokxq5dUalltEZTUKek5uGHH2bMmDGMGDHioqSmrgoKCoiIiMBms9G9e3fmzp1LTEwMAFarlT179vDMM89UumbkyJFs27bNIfULIURL4KZ3AyDfmo+PqWGn5x7fth7NE88QVQy89jf8b7izXurpMqwPKXfegX7dt7irZ1cct5w6ReID07FbLUR89hmm6C7YS0ooy8jElpFOWWYmZRmZlGVmUHouidx168j66CO0AQG49OmNz733YoyKqpeYhWPUOqlZvXo1e/fuZdeuXQ4LokOHDixfvpzo6Gjy8vJ4++23GTBgAAcOHKBdu3ZkZGRgs9kICKi86E9AQAApKZfeT8RisWCxWCp+zstr+GmMQgjRmLjqyndXTi5IbrCkRlEUtix6Fp8P1lIUZCRk+Ye0irqqXuv0njyZ7E8+Jffbb/EaP56CrVtJemI2uqAgIj5ZgS44GAC10Yg+NARCL97DSbHZKN67l6xPPiVv3bcUbNlK1M4d9Rq3uDK1SmoSExOZOXMmGzZsqDTe5Ur17duXvn37Vvw8YMAAevTowaJFi1i4cGHF8f8dQKYoymUHlc2fP585c+Y4LE4hhGjqciw5APyc9DPRftH1Xl9RXjZbZk4kcns8scPacM3rn2Fyca/3evVhYbgOG0b2ik+wZWaS/vZCXIcOJfi1V9G4utaoDJVGg8tVV2Hq1o1TIw+i8fSs36DFFavVS8I9e/aQlpZGz5490Wq1aLVatm7dysKFC9FqtdhsNscEpVZz1VVXERsbC4Cvry8ajeaipzJpaWkXPb35q2effZbc3NyKT2JiokPiE0KIpqqjT/kmjF4GL9KL0skoziCrJIuckhxyLbnkW/MpLC2kqLSIUnspZfYySm3lv9rsNhRFqXFdcYd+ZcfYYQTvjif5idsY+953DZLQXOA9+U4ssbGkL1yEz/QHCH33nRonNH+l0usJePopLMePU/I/E1pE41KrJzXDhw/n0KFDlY5NnTqVDh068PTTT6Nx0IhxRVHYv38/0dHl/4vQ6/X07NmTjRs3ctNNN1Wct3HjRsaNG3fJcgwGAwaD4ZLfCyFESxPmFkYP/x7M3zmf+TtrP8miV0Avlo5cilZ9+dvHryv+icuC5ag9dJiWL2J4zxF1DbnOXPr2xf/pp3Hp1RNT9JU9lXIbMQKNry+5a77G+Owz1V8gnKJWSY2bmxtdunSpdMxsNuPj41NxPCsri4SEBJKTkwEqpmkHBgZWzG666667CAkJqZi1NGfOHPr27Uu7du3Iy8tj4cKF7N+/n3fffbeinscff5zJkyfTq1cv+vXrx5IlS0hISGD69Ol1bLoQQrRMrw99neNZx7ErdhRFwa7YsVP59yezTrL00FIAZvaYiZ/Jj3xrPq/tfo1Pj37KlC5TqizbWlLExqfupPWGY5zqFcSQRatw93LOJogqlQqfqVMcU5ZOh/vIa8jftIkASWoaLYfv/bRu3TqmTp1a8fOECRMAeOGFF3jxxRcBSEhIQP2X6XE5OTncf//9pKSk4OHhQUxMDD///DO9e/+5mdjtt99OZmYmL730EufPn6dLly58//33REREOLoJQgjRrPmafBkYMvCy51zb6lomdZyEWqXGy+hVcTypIInFBxZzTatrCHGtPLg2+dQBjj50L+HnCkmYdi1jHnu90r/1TZ25f3+yP1tFaXJyxUBjAMVqxV5SgtrVVaZ+O5lKqc0L0iYuLy8PDw8PcnNzcXdvuPe6QgjRXBSWFjLum3G092rPu8PfrZissXPtElQvvoVVr8b1ny/SbeitTo7U8coyMogdOIjAl+agNhjI/2kzxYcOUpaSCnZ7+dOc66/H5957arR3lai5mt6/JakRQghRKz8l/MTMzTN5bchrjAi5mo0vTiP8q50kdPKm9+KV+AS2cnaI9UJRFGL79sOWmwuAsWtXzH16owsPR+PqijU+gexVqyhLS8P3kRn4Tp8uT24cRJKaKkhSI4QQjjHzp5nsO7GVp7/X0+pUPvG39WPU35eg0Th8VEOjkrt+Pfb8fFyHXY0uwP+i7xWrlYwPlpCxeDGugwcTumghKn3Dr9zc3EhSUwVJaoQQwjHObdvE2ZmPoLWB5qXZXHX9vc4OqVEp+PlnEh+egfddkwl48klnh9Pk1fT+Lc/FhBBC1JiiKGQuX07+/Y/hG9mJyK/XSEJTBdfBg/GfNZOsZR9SuH27s8NpMSSpEUIIUSO2/HySZs4i7R//xHvyZKJWriYgoqOzw2q0vKdOxaVXL1Jfe83ZobQYktQIIYSoVsnx48TdeiuF27YRsvBtAp5+CpVO5+ywGjWVWo33PfdgOXqMkuPHnR1OiyBJjRBCiMvKWfM1Z2+fgNrkQuRXX+I+cqSzQ2oyXAcNROPjQ+633zo7lEsqtSssTUwn+rfDXLPrBF+kZDk7pDpr3sPUhRBC1Jm9pISUefPI/fIrPG69hcDnnkPtwM2MWwKVToepe3csJ2OdHcol/TPuPIsT0rgt0JuM0jJmHksgzKinn2ft98lyNklqhBBCXMQaH8+5mbOwxsUR9PLLeN5ys7NDarL0ERHkb9zo7DCqpCgK36XncGewD69GhWFTFG7df4oZR+P571VReOqaVpogr5+EEEJUUrR7N3G33Ir17FnCPnhfEporpG8VQWlSEorV6uxQLpJXZuNssZWubi4AaFQq3ukYQYHNzt9PJTk5utqTpEYIIUQltpwc1K6uKCUlJD74EPaiImeH1KTpI1qB3Y41MdHZoVzEQ6clxs2F9ek5FcdCjHqebR3Ev1OyOVZQ7Lzg6kCSGiGEEJW4jRhBuy2bcb/hBrR+fqhMJmeH1KRpfbwBKrZXaGzuCvFhS1Y+23MKKo7dEeRNuFHPP+POOzGy2pOkRgghRJWK9+zBbdiwik0rRR1d+PNrpAv43xzgxQBPVyYdPMOu3EIA9Go1T0YG8kNGHnv+ONYUSFIjhBDiInaLhdLkZAzt2zk7lKavkSc1BrWaFV1b09ls4rHjCVzYPemmAC86mI28cuY8TWVHpaY1rFkIIUSDUOn1qM1myrKa7poljUfVT7oUm43iffvI37wZAF1AAFp/fwp/20bx4cOUZaRjLyxCHxKMuf8A3K8fgyk6ul4idNGoeaxVAHccPMPxwhI6uprQqFQ82zqIuw/F8Ut2AYO93eqlbkeSpEYIIcRFVCoVhg4dKDl02NmhNH0Xcpq/PO0oOXmSpMcfx3rqdPm4JaOR0r8MJDb16IHXhPIFD61xZ8j9fj1ZH3+M+9gbCJozB3U9jHOKMJXvJp5bZqs4NtLHnWhXEx8kpktSI4QQoulyHTSIjCVLsJeUyKJ7V0Cxlpb/Rlt+y837z39Ifvb/0IeHE/DWWyzJKGBLqYLVbqf/wb3cvvE7ivfuxZadjXnAADSeXnjecgtZKz4hb9236EPD8Hv0EYfHWWizA+XTui9QqVRMDfHliROJxBdbiDAZHF6vI8mYGiGEEFVyv3YUSlERed995+xQmrSy1BSg/PVS/k+bSZr9JG7Dh+N1//3MOJ7IosAIvF1diGzdiuU3TuCpZasxfrAEU0wMhb/9Rt4PP5C7bh264CDcr78e9zHX1UucP2Tk4qZRE+1a+SnQjQFeeGg1LE/KqJd6HUme1AghhKiSvlUr3K4ZQcaSpbiPHYtar691GYqiUHLgAIYOHVrs057S8+VJTdGePZx/7nnMA/qjdnHhqxWr+OnB2XzQypdxkb0A2JtXyD2HznKHwYfPn/87bRroyYiiKHyTmsN1fp4YNZWfd7ho1Nwe5M3q81k8FRmESdN4n4c03siEEEI4ne+MRyg9f57011+v1XWKolC4fTtnrhvD2QkTKfjpp3qKsOlIfvIptP7+lJ1PIffbb3nuwdkAjIsMrTinh7uZtT3aogBj957iRGFJg8S2O6+IM8UWbgrwrPL7KcG+ZJfZ+CYtu0HiqStJaoQQQlySMao9AU/OJuvjFeR+W7PXUNaEBOLvnEzC1HuwxsUB4DpkSH2G2ah53jaeNj/+QOg7i1AsFsqys2n12UrG+HkAUPzHWJYLIkwG1sa0w0un4ca9sezLq/8VnV+NO08Hs5FBXlUPBo50MTDM242PkjIa9fRuSWqEEEJcltfkyXiMG0fyU0+R8f77KHZ7lecpikLOmq+Ju/EmylJTCVu6BGOnTnjcdBNqs7mBo248VCpV+aaWmzdjy88nfNm/MHbqxHOtg1EBX6ZePG0+wKDj65i2tHExMH7/qYpF8erDL1n5/JJdwDORQZUGCf+vqSG+HMwvbpAkq65kTI0QQojLUqlUBM1/BV1ICOlvLyTv+//gcdNNGDt3Qm02Y8vIoPjAQXLXf0dpfAIeN95IwHN/Q+PqStITs3Ebfa2zm+B0JSdPkrvmawJf+DvGqCig/OnHdX4evJeQzsRAH7TqygmFp07L6m5tuPPgGSYcOM2nXVvTz9PVoXEpisL8uPPEuLkwytf9sucO93En1Kjjw6QMeng0ziRVkhohhBDVUqnV+D36CK6DB5H+3nukv/02Ssmf4z3Urq64jRyJx9y5mHv3/vPCsjJUWp0TIm5csj/5FK2/P5633FLp+KyIAK7ZfZLVKVncGexz0XWuWg0ru7Xm7oNx3HHgDJ90jWTgJV4R1cV/MnLZm1fEF93aVLsdhkal4u5gX16LS+HFtiH46htfCtH4IhJCCNFombp3J/yDD1BKS7EmJmIvLELr54vW1xeV9uJbitrdHVtOTsMH2ojYi4rIXb8en3vuQaWrnOBFu7lwk78nC+JSuDnAC5cqZhaZNRo+6dqaqYfiuPPgGR4I8yfKbKSD2Ug7FyM6dd325rLa7cw9ncxQL7caL6x3R5APC86msOp8Jo9EBNSp3vokSY0QQohaU+l0GFq3rvY8fUQE1vj4Boio8Srcvh2lqAiP68dU+f3TrYMYuOMY7yWk8URkYJXnmDRqlkdH8vTJc6w6n0matQwAnUpFe7OBKLMJP50WH70WH50W3z9+9fnjmJtGXelJTJld4Z9xKcQXW/mwS2SN2+Kj1zLW35PlSRlMD/Ovc0JVXySpEUIIUW8MbVpT+Ns2Z4fhVPmbN6OPjETfqlWV37cyGZgRHsCCsym0MxsZ6+9Z5XlGjZq3O4YDkFtaxrHCEo4UFHO0oJjTRRYO5ReRYS0j+y/bHFzgrlXT3sVIO7MRd42GTZl5nC628H+tg+joWrstFx4M8+ffKdlsyMxljF/VsTqLJDVCCCHqjUu/fmR/tgrr2bOXvKk3Z4rNRsGWrXiMHXvZ856KDCSxxMqMo/F46zTVjpvx0Gnp6+lK3yoGDpfZFbLLysiwlpFZWv5rfLGV2KIStucUcLbYykgfd97rHEFXN5dat6mTq4mubia+SsmWpEYIIUTL4TpwIBpvbzKXLyfoxRedHU6Dy1rxCbbMTNxHj77seWqVijc7hJFpLWPKoTi+jmlLdB0SDgCtWoWfXoef/uIB2ha7naSSUlq7XNlKxbcGeDHv9HmyS8vw0jWeVELWqRFCCFFv1CYT3lOmkPvVGkpTU50dToMqPnCA9DfewPvuuzFFd6n2fL1azbIurWjjYuCOg2eIL7Y4PCaDWn3FCQ3Ajf5elCkK36XnXHlQDiRJjRBCiHrldcdEVC4uZP5rmbNDaTBlGRmce3Qmxs6d8X/8sRpfZ9Zq+LRra9w0Gm4/cJr0Czt8NzL+Bh1DvN34KqVxbZsgSY0QQoh6pXF1xWfK3WSvXk3J0aPODqdeKaWlZK/+nDM33YRisxHy9tuoarkRqJ9ex6purSmy2Zl04AwFVQz8bQxi3F1IKLE6O4xKGs+LMCGEEM2Wz733kvfjBpKeeorIL790yo7dit2OLSuLop07sZw9i7FDRxSrBcViQSktRaXXozIYURn0qI3G8o+7OxpPTzReXpddnE5RFPI3bCT9zTexxsfjfv31+M2ciS7Av06xRpgMrOrWhhv3xjLhwGle7xBOlLlx7XJuVKspucSWGc4iSY0QQoh6p9LrCX7lZeJuvoXMJUvwe/TReqnHlpeH9cwZLGfisJ45jfVcEmWpqZSmplCWngGldXud43r11YS++06ViU3Rrl2kLlhAyYGDmAcOJOTNNzB27HilTaGzq4mVXVvz6PEErt51nLuCfZndKhCfRrKSr16lwmpvXJtbNo4/GSGEEM3ehV2+DR06OLRcW0EBlmPHyF2/npzVn1cc14WEoAsPQx8ejstVV6EN8EcXEIBKr0fj4YEuJASVwVD+0WpRrFYUiwV7iQXFUoK9uAR7fh4lx46T+vLL5K5ZU2mbg5ITJ0l/4w0Ktm7F2Lkz4R99iLlfP4e2rbenK1t7d2DZuQzePJvCV6lZPB4RyD2hvujVzh1BYtCosUhSI4QQoqUp+O03sj76CP+nnsJ95EiHlp08+0kKtmxBZTTi99hjuA4ehL5VK9Sm2i0qpzIawWhE41H5uEuvXpQcOULqP/6JeeAgsJWRvnARuWvXogsLI+TNN3AbNQpVPSUZBrWah8L9GR/oxWtxKbx0OpmPkzN4vk0wo309qt2zqb7klpY1uhWFVYqiNK40qx7l5eXh4eFBbm4u7u6X341UCCGE48Tdcitqk4nwFR879OZvy88ndvAQPG4cR8CTT6J2qdvaLtXWk5PD6etvQOPuTum5c6jd3PB9+CG8xo+/aD+n+nasoJg5p5LZkp1PF1cTsyICuM7PA3UDJzdj98bio9PyUXTNt1moq5rev2X2kxBCiHpliY2l5MgRvKdOcfjTjNy161CsVnynP1hvCQ2AxtOToDkvYsvNxeeB+2m74Ue877ijwRMagI6uJlZ1a81X3dvgpdNw35GzDNl5nBVJGRQ20EypVEspu3MLGeHTuB4QyOsnIYQQ9apw507Q6TAPHOjQcm0FBWQuXYrbyGvqPMuoNtyGD8dt+PB6r6cmVCoVA7zcGODlxu7cQt5JSOWZk+f4+6kkerib6etppp+HKz08XDBrNA6tO6HYwh0Hz+Cl0zLK16P6CxrQFaXM8+fPR6VSMWvWrIpja9asYdSoUfj6+qJSqdi/f3+15SxdupRBgwbh5eWFl5cXI0aMYOfOnZXOefHFF1GpVJU+gYFV72YqhBCi8Sg5dBhjhw6oDVe+ku0F9sJCzj30MPb8fAJmz3ZYuU1RLw8zy6Nbs7NfJ56JDMJDq+GjcxmMP3CaqF8Ocd2ek8w8lsDc08m8n5DGlylZ/Jqdz9liC5ZaTMm2KwobM3K5YW8spXaF73q0w7eRzMS6oM7R7Nq1iyVLltC1a9dKxwsLCxkwYADjx49n2rRpNSpry5YtTJw4kf79+2M0Gnn11VcZOXIkR44cISQkpOK8zp07s2nTpoqfNQ7OPoUQQjieLScHrZ+fw8orTUri3MxZWM+cIWzpEnR/uU+0ZKFGPdPD/ZlOeQJysqiE33MK2ZFTwOmiEn7PKSO9tIwiW+VEJlCvo5OrkfZmIxEmAxFGPeEmPWFGPVqVinRrGd+l5/DhuQzOFFvo62FmaZdWVe4t5Wx1SmoKCgqYNGkSS5cuZd68eZW+mzx5MgBnz56tcXkrV66s9PPSpUv58ssv+e9//8tdd931Z7BarTydEUKIJkax2VA58D+h6QsXUXr+POGfrMDUubPDym1O1CoVHcwmOphNTAnxrfRdoc1GqqWMcyVWkixWzhZbOZxfzI8ZuSSWWCn7Y/rQhWHHCqBRwfV+nrzdMZxe7i5Om3FVnTolNQ8//DBjxoxhxIgRFyU1jlBUVERpaSne3t6VjsfGxhIcHIzBYKBPnz688sortG7d+pLlWCwWLJY/NwTLy8tzeKxCCCEuTxcSTPH+Aw4sUIsuOFgSmjoyazS0dtFUubGlTVE4byklvthCfLEVO+Cv1xLtZiLIULvtHpyh1knN6tWr2bt3L7t27aqPeAB45plnCAkJYcSIERXH+vTpw4oVK2jfvj2pqanMmzeP/v37c+TIEXx8fKosZ/78+cyZM6fe4hRCCFE9fXgEueu+RVEUx/0P39Y490Nq6jQqFaFGPaFGPQO8nB1N7dVqoHBiYiIzZ87k008/xVhP+3a8+uqrrFq1ijVr1lSqY/To0dxyyy1ER0czYsQI1q9fD8DHH398ybKeffZZcnNzKz6JiYn1ErMQQohL07eKQCkqojQpySHlFe/bj1Ge0ogq1Cqp2bNnD2lpafTs2ROtVotWq2Xr1q0sXLgQrVaL7Qoz5wULFvDKK6+wYcOGiwYg/y+z2Ux0dDSxsbGXPMdgMODu7l7pI4QQomG59O6DymQi77vvrris0tRUrKdPY+7v2O0IRPNQq6Rm+PDhHDp0iP3791d8evXqxaRJk9i/f/8VzUZ67bXXmDt3Lj/88AO9evWq9nyLxcKxY8cICgqqc51CCCHqn8bVjPuoUWR/8QWK1XpFZeX/97+g1Tp8jyXRPNRqTI2bmxtdunSpdMxsNuPj41NxPCsri4SEBJKTkwE4ceIEAIGBgRUzl+666y5CQkKYP38+UP7K6fnnn+ezzz6jVatWpKSkAODq6oqrqysAs2fP5oYbbiA8PJy0tDTmzZtHXl4ed999d13bLoQQooF43zOV3LVryVm7Fq/x4+tcTv5/fsDcty8aT0/HBSeaDYdvk7Bu3TpiYmIYM2YMABMmTCAmJob333+/4pyEhATOnz9f8fPixYuxWq3ceuutBAUFVXwWLFhQcc65c+eYOHEiUVFR3Hzzzej1en7//XciIiIc3QQhhBAOZmzfHvfR15L+xpuU/uXf/9oo3LmTol278LjpRscGJ5oN2dBSCCFEgyjLzubsLbei8fYmfPlHaP54El8Ttpwc4m67Ha2XFxGrPqu3HbFF4yQbWgohhGhUtF5ehCxaiDU+nrMTJmCNj6/RdfaSEhIfehh7Xh7Br70qCY24JPmbIYQQosGYOnem1eefQ5mNuPG3kbViBaVpaZc833ImjsQHH6Tk6FHC3n8PfXh4A0Yrmhp5/SSEEKLB2fLySJnzEnkbNkBZGS69euHSpw+Gdu1QaTVYTp6k5OhR8n/ajDbAn6C5c3EdMMDZYQsnqen9W5IaIYQQTmPLzSV/03/J2/AjJQcPYcvOBkDt4YGxXTvcRl6D54QJqPWNf4l+UX8kqamCJDVCCNG4lWVkoNjsaP39Gu2miaLh1fT+XacNLYUQQoj6oPX1rf4kIS5BBgoLIYQQolmQpEYIIYQQzYIkNUIIIYRoFiSpEUIIIUSzIEmNEEIIIZoFSWqEEEII0SxIUiOEEEKIZkGSGiGEEEI0C5LUCCGEEKJZkKRGCCGEEM2CJDVCCCGEaBYkqRFCCCFEsyBJjRBCCCGahRa1S7eiKED5FuZCCCGEaBou3Lcv3McvpUUlNfn5+QCEhYU5ORIhhBBC1FZ+fj4eHh6X/F6lVJf2NCN2u53k5GTc3NxQqVQOLTsvL4+wsDASExNxd3d3aNmNnbRd2i5tbzmk7S2v7Y2h3YqikJ+fT3BwMGr1pUfOtKgnNWq1mtDQ0Hqtw93dvUX9Zf8rabu0vaWRtkvbWxJnt/tyT2gukIHCQgghhGgWJKkRQgghRLMgSY2DGAwGXnjhBQwGg7NDaXDSdml7SyNtl7a3JE2p3S1qoLAQQgghmi95UiOEEEKIZkGSGiGEEEI0C5LUCCGEEKJZkKRGCCGEEM2CJDU1sGXLFlQqVZWfXbt2AZCZmcm1115LcHAwBoOBsLAwZsyYUe0+U0OHDr2ozAkTJjREs2qkPttusVh45JFH8PX1xWw2M3bsWM6dO9cQzaqRmrT9wIEDTJw4kbCwMEwmEx07duTtt9+utuzm0O91bXtz6HeAmTNn0rNnTwwGA927d69R2c2h36FubW8u/Z6QkMANN9yA2WzG19eXRx99FKvVetmym0u/16XtDd7viqiWxWJRzp8/X+lz3333Ka1atVLsdruiKIqSlZWlLF68WNm1a5dy9uxZZdOmTUpUVJQyceLEy5Y9ZMgQZdq0aZXKzsnJaYhm1Uh9tn369OlKSEiIsnHjRmXv3r3KsGHDlG7duillZWUN0bRq1aTty5YtUx555BFly5YtyunTp5VPPvlEMZlMyqJFiy5bdnPo97q2vTn0u6IoyiOPPKK88847yuTJk5Vu3brVqOzm0O+KUre2N4d+LysrU7p06aIMGzZM2bt3r7Jx40YlODhYmTFjxmXLbg79Xte2N3S/S1JTB1arVfH391deeumly5739ttvK6GhoZc9Z8iQIcrMmTMdGF39clTbc3JyFJ1Op6xevbriWFJSkqJWq5UffvjBYfE6Uk3b/tBDDynDhg277DnNtd+ra3tz7PcXXnihVklNc+r3mra9ufT7999/r6jVaiUpKani2KpVqxSDwaDk5uZesqzm0O91absz+l1eP9XBunXryMjIYMqUKZc8Jzk5mTVr1jBkyJBqy1u5ciW+vr507tyZ2bNnV+wm3hg5qu179uyhtLSUkSNHVhwLDg6mS5cubNu2zZEhO0xN2g6Qm5uLt7d3teU1t36H6tvenPu9pppjv1enufT79u3b6dKlC8HBwRXHRo0ahcViYc+ePZctr6n3e13a7ox+b1EbWjrKsmXLGDVqFGFhYRd9N3HiRNauXUtxcTE33HAD//rXvy5b1qRJk4iMjCQwMJDDhw/z7LPPcuDAATZu3Fhf4V8RR7U9JSUFvV6Pl5dXpeMBAQGkpKQ4PG5HuFzbL9i+fTtffPEF69evv2xZzanfL6hJ25trv9dUc+z3mmgu/Z6SkkJAQECl87y8vNDr9ZdtR3Po97q03Sn9Xi/Pf5qIF154QQEu+9m1a1elaxITExW1Wq18+eWXVZZ5/vx55dixY8o333yjdOrUSXnwwQdrFdPu3bsVQNmzZ0+d21UTzm77ypUrFb1ef9HxESNGKA888MCVNa4a9dF2RVGUw4cPK35+fsrcuXNrHVNT7ndFqXnbm2O/1+b10/9q6v1e07Y3l36fNm2aMnLkyIvq0Ol0yqpVq2ocU1Ps97q03Rn93qKf1MyYMaPaEeitWrWq9PNHH32Ej48PY8eOrfL8wMBAAgMD6dChAz4+PgwaNIjnn3+eoKCgGsXUo0cPdDodsbGx9OjRo0bX1IWz2x4YGIjVaiU7O7tSFp+Wlkb//v1r36BaqI+2Hz16lKuvvppp06bx3HPP1TqmptzvtWl7c+v3K9WU+702mku/BwYGsmPHjkrHsrOzKS0tvegpxuU0xX6vS9ud0u/1kio1U3a7XYmMjFSeeOKJGp3/888/K4ASFxdX4zoOHTqkAMrWrVvrGGX9cHTbLwwg+/zzzyuOJScnN8qBg9W1/fDhw4q/v7/y5JNP1rmOptrvtW17c+r3C67kSU1T7fcLajtQuKn3+4XBssnJyRXHVq9eXe1A4f/VFPu9Lm13Rr9LUlMLmzZtUgDl6NGjF323fv165cMPP1QOHTqkxMXFKevXr1c6d+6sDBgwoOKcc+fOKVFRUcqOHTsURVGUU6dOKXPmzFF27dpVcU2HDh2UmJiYRjPN8QJHt11Ryqf6hYaGKps2bVL27t2rXH311Y1qiucFl2v7hdcukyZNqjQdMi0treKc5trvdWm7ojSPflcURYmNjVX27dunPPDAA0r79u2Vffv2Kfv27VMsFouiKM233xWl9m1XlObR7xemNQ8fPlzZu3evsmnTJiU0NLTStObm2u91abuiNHy/S1JTCxMnTlT69+9f5Xc//fST0q9fP8XDw0MxGo1Ku3btlKefflrJzs6uOCcuLk4BlM2bNyuKoigJCQnK4MGDFW9vb0Wv1ytt2rRRHn30USUzM7MBWlM7jm67oihKcXGxMmPGDMXb21sxmUzK9ddfryQkJNRzS2rvcm2/1DvriIiIinOaa7/Xpe2K0jz6XVHKp+lW1f4LTyeba78rSu3brijNp9/j4+OVMWPGKCaTSfH29lZmzJihlJSUVHzfnPu9tm1XlIbvd5WiKEr9vNgSQgghhGg4sk6NEEIIIZoFSWqEEEII0SxIUiOEEEKIZkGSGiGEEEI0C5LUCCGEEKJZkKRGCCGEEM2CJDVCCCGEaBYkqRFCCCFEsyBJjRBCCCGaBUlqhBBCCNEsSFIjhBBCiGZBkhohhBBCNAv/D+6DOFUQTW/JAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with island triage - RUN ONLY WITH ISLANDS\n",
    "print(\"If you like my proposed translations, let's rebuild the MAP with the adjusted county geoms\")\n",
    "print(\"This would need adjustment for multi-county islands like Michigan UP\")\n",
    "for c in range(nCounties):\n",
    "    if hasIsland[c] :  #rebuild the county geom with the translated islands\n",
    "        countyGeom[c] = dummyPoly\n",
    "        for t in countyTractList[c]:\n",
    "            if t not in skipList:\n",
    "                if countyGeom[c] == dummyPoly:\n",
    "                    countyGeom[c] = tractGeom[t]\n",
    "                else:\n",
    "                    countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "MAP = countyGeom[0]\n",
    "plotPoly(countyGeom[0])\n",
    "for c in range(1,nCounties):\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "    plotPoly(countyGeom[c])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4aebe4ee-ee7b-4bf3-8413-f315556d453e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing all county centerpoints, then plot them\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"computing all county centerpoints, then plot them\")\n",
    "countyCP  = list()\n",
    "cutCountyList = list()\n",
    "for c in range(nCounties):\n",
    "    cTL = countyTractList[c]\n",
    "    countyCP.append(getHDcp( [tractCP[v] for v in cTL], [tractPop[v] for v in cTL], [i for i in range(len(cTL) ) ] ) )\n",
    "    if countyCP[c].disjoint(countyGeom[c]):  #possible with weird-shaped county\n",
    "        countyCP[c] = nearest_points(countyGeom[c],countyCP[c])[0]\n",
    "    plotPoly(countyCP[c].buffer(0.03))\n",
    "    plotPoly(countyGeom[c],0.5)\n",
    "plotPoly(MAP)\n",
    "plt.show()   \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "207f3a55-e361-4777-ae46-41fe3d54a4b8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This code block identifies whole districts to be cut out of the map\n",
      "For MA my input file says to cut out 1 districts out of 9\n",
      "performing cut no 0 for state MA\n",
      "the total proposed cutPop is 778232.0 . Compare to 781101\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to apply the cut 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are your cut districts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# updated NCUTDISTRICTS (district slice-out) code vs. vanillaRefine - this WORKS\n",
    "print(\"This code block identifies whole districts to be cut out of the map\")\n",
    "unclippedMAP = MAP\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "stateRows = trimDF[\"state\"].to_list()\n",
    "DFrow = stateRows.index(STATE)\n",
    "nClips = trimDF[\"nClipPoly\"][DFrow]\n",
    "nCuts  = trimDF[\"nCutPoly\"][DFrow]\n",
    "nCutDistricts = nCuts\n",
    "nStops = trimDF[\"nStopLines\"][DFrow]\n",
    "nDistricts = trimDF[\"nDistricts\"][DFrow]\n",
    "stopLines = list()  #the 'stoplines' stop wedge growth that hops across concave map areas (not currently used)\n",
    "cutCountyList = list()\n",
    "allCutLists, allCutPops = list(), list()\n",
    "print(\"For\",STATE,\"my input file says to cut out\",nCuts,\"districts out of\",nDistricts)\n",
    "if nCuts > 0:\n",
    "    cutList = list()\n",
    "    cutXs = ast.literal_eval(trimDF[\"cutXs\"][DFrow])\n",
    "    cutYs = ast.literal_eval(trimDF[\"cutYs\"][DFrow])\n",
    "    cutPoints = [Point(cutXs[i],cutYs[i]) for i in range(len(cutXs)) ]\n",
    "    for cutNo in range(nCuts):\n",
    "        print(\"performing cut no\",cutNo,\"for state\",STATE)  #first two cutPoints must form the cutLine\n",
    "        cutNotDone = True\n",
    "        thisCutPoints = [ cutPoints[int(4*cutNo)],cutPoints[int(4*cutNo+1)],cutPoints[int(4*cutNo+2)],cutPoints[int(4*cutNo+3)]  ]\n",
    "        while cutNotDone:\n",
    "            cutPoly = Polygon([thisCutPoints[0],thisCutPoints[1],thisCutPoints[2],thisCutPoints[3] ])\n",
    "            cutLine = LineString([thisCutPoints[0], thisCutPoints[1] ] )\n",
    "            cutList = list()\n",
    "            cutPop = 0.\n",
    "            for c in range(nCounties):\n",
    "                if cutPoly.intersects(countyGeom[c]):\n",
    "                    if cutPoly.contains(countyGeom[c]):\n",
    "                        cutList = cutList + countyTractList[c]\n",
    "                        cutPop += countyPop[c]\n",
    "                    else:\n",
    "                        for t in countyTractList[c]:\n",
    "                            if cutPoly.contains(tractCP[t]):\n",
    "                                cutList.append(t)\n",
    "                                cutPop += tractPop[t]\n",
    "            print(\"the total proposed cutPop is\",cutPop,\". Compare to\",int(statePop/nDistricts) )\n",
    "\n",
    "            doIcut = input(\"enter 1 to apply the cut\")\n",
    "            if str(doIcut) == str(1):\n",
    "                cutNotDone = False\n",
    "                allCutLists.append(cutList)\n",
    "                allCutPops.append(cutPop)\n",
    "            else:\n",
    "                print(\"Here is the state and the last proposed cutPoly.\")\n",
    "                plotPoly(cutPoly)\n",
    "                plotPoly(MAP)\n",
    "                plt.show()\n",
    "                print(cutPoly)\n",
    "                oldPts = list(cutPoly.exterior.coords)\n",
    "                newPtXs = ast.literal_eval(input(\"enter alternate [ x0, x1 ] for the first two points\") )\n",
    "                newPtYs = ast.literal_eval(input(\"enter alternate [ y0, y1 ] for the first two points\") )\n",
    "                thisCutPoints = [Point(newPtXs[0],newPtYs[0]), Point(newPtXs[1],newPtYs[1]),oldPts[2], oldPts[3] ]\n",
    "allCutVTDs = list()\n",
    "for L in allCutLists:\n",
    "    for v in L:\n",
    "        allCutVTDs.append(v)\n",
    "if nCutDistricts == 0:\n",
    "    print(\"There were no cut districts in\",STATE)\n",
    "else:\n",
    "    print(\"here are your cut districts\")\n",
    "    plotPoly(MAP)\n",
    "    for i,L in enumerate(allCutLists):\n",
    "        cutGeo = tractGeom[L[0]]\n",
    "        for v in L:\n",
    "            cutGeo=cutGeo.union(tractGeom[v])\n",
    "        plotPoly(cutGeo,2)\n",
    "        plotCenter(i,cutGeo)\n",
    "        plotCenter(allCutPops[i],Point(cutGeo.centroid.x,cutGeo.centroid.y-0.5) )\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4c915415-6c44-4c43-9826-b565cebed95a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Write the vtd cutlists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Write the vtd cutlists to a file\")\n",
    "cutDistrictNo = list()\n",
    "for v in allCutVTDs:\n",
    "    for i,L in enumerate(allCutLists):\n",
    "        if v in L:\n",
    "            cutDistrictNo.append(i)\n",
    "            break\n",
    "nbrDF = pd.DataFrame( {\"vtdNo\":allCutVTDs,\"cutDistrictNo\":cutDistrictNo} )\n",
    "outname = STATE+\"wholeDistrictCuts.csv\" \n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "nbrDF.to_csv(outpath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "17721728-e5c0-4787-b430-9d7d94970667",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "countyPop = [0. for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    countyPop[countyNo[t]] += tractPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "02f95c46-5073-4b9d-817c-16a57d5ebb52",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n"
     ]
    }
   ],
   "source": [
    "print(nDistricts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f3073320-8d92-45ba-beb2-fb8c08d7170d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed all whole districts.  Finalize stats before skewing outcroppings.\n",
      "Of the total statePop 7029917.0 we ignore 778232.0 as it is cut out of the map\n",
      "This excluded pop comes from a total of 229 tracts\n",
      "Our final adjusted number of state districts, excluded fixed cut-out is 8 rather than original 9\n",
      "Each district will be drawn to house 781460.625 = 1/ 8 of non-cutout state pop 6251685.0 vs original= 7029917.0\n",
      "Here is a county-level modified map with each county's fraction of a district pop\n",
      "working on county 0\n",
      "here is the MA map excluding cut and unpop coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Removed all whole districts.  Finalize stats before skewing outcroppings.\")\n",
    "trueCountyPop = [countyPop[c] for c in range(nCounties)]\n",
    "trueCountyGeom = countyGeom.copy()\n",
    "countyPop = [0. for c in range(nCounties)]\n",
    "trueStatePop, true_nDistricts = np.sum(trueCountyPop), nDistricts\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "# minTractPop = 4.5  #already defined above\n",
    "populatedTractList = list()\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "statePop, excludedPop = 0., 0.\n",
    "\n",
    "for t in range(nTracts):\n",
    "    trueCountyTractList[countyNo[t]].append(t)\n",
    "    if t in allCutVTDs or t in skipList:  #  or vtdPop[t] < minTractPop:  #need to keep low-pop vtds as VEST may have assigned votes to them\n",
    "        excludedPop += tractPop[t]\n",
    "    else:\n",
    "        populatedTractList.append(t)\n",
    "        countyTractList[countyNo[t]].append(t)\n",
    "        countyPop[countyNo[t]] += tractPop[t]\n",
    "        statePop += tractPop[t]\n",
    "        #tractPop[t] = max(tractPop[t],0.000001)  #avoid possible later div/zero errors for nil-vote vtds\n",
    "print(\"Of the total statePop\",statePop+excludedPop,\"we ignore\",excludedPop,\"as it is cut out of the map\") #,\n",
    "#\"or in sparsely populated areas (<\",minTractPop,\") per geom\")\n",
    "print(\"This excluded pop comes from a total of\",nTracts -len(populatedTractList),\"tracts\")\n",
    "true_nDistricts = nDistricts\n",
    "nDistricts -= nCutDistricts\n",
    "print(\"Our final adjusted number of state districts, excluded fixed cut-out is\",nDistricts,\"rather than original\",true_nDistricts)\n",
    "aDP = statePop/float(nDistricts)\n",
    "print(\"Each district will be drawn to house\",r3(aDP),\"= 1/\",nDistricts,\"of non-cutout state pop\",statePop,\"vs original=\",trueStatePop)\n",
    "print(\"Here is a county-level modified map with each county's fraction of a district pop\")\n",
    "\n",
    "vtdGeom = tractGeom.copy()\n",
    "nVTDs = len(vtdGeom)\n",
    "\n",
    "cutCountyList, uncutCountyList = list(), list()\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"working on county\",c)\n",
    "    if len(countyTractList[c]) == 0:  #no vtds added to county b/c all were in cut lists:\n",
    "        cutCountyList.append(c)\n",
    "    else:\n",
    "        uncutCountyList.append(c)\n",
    "        countyGeom[c] = tractGeom[countyTractList[c][0]]\n",
    "        for t in countyTractList[c]:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "uncutMAP = MAP\n",
    "MAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "print(\"here is the\",STATE,\"map excluding cut and unpop coastal tracts\")\n",
    "\n",
    "for c in uncutCountyList:\n",
    "    plotPoly(countyGeom[c])\n",
    "    FONTSIZE = 12\n",
    "    if countyPop[c] / statePop < 0.02:\n",
    "        FONTSIZE = 7\n",
    "        plotCenter(r3(countyPop[c]/aDP),countyGeom[c], FONTSIZE)\n",
    "    else:\n",
    "        plotCenter(round(countyPop[c]/aDP,2),countyGeom[c], FONTSIZE)\n",
    "plotPoly(trueMAP,0.1)\n",
    "for c in cutCountyList:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "    plotCenter(\"cut\",countyGeom[c],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0062ac21-3328-4616-99ed-70c53e62b308",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now finalize the map topology before any squish operations.  Plot countyPop/aDP\n",
      "Working on county  0 distances\n",
      "the max LAT-scaled distance between counties is 2.57163\n"
     ]
    },
    {
     "data": {
      "image/png": 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EwiIMOHa7nc8++4yjR4+ybNkyJk6c6OqQBGFA8vb25p577iEkJIR3332XnJwcV4ckDGAiYREGFKvVyocffsjJkye59dZbSUtLc3VIgjCgeXh4cNdddxEdHc17773H6dOnXR2SMECJhEUYMEwmE++99x45OTnccccdopS4IPQRnU7HHXfcQUJCAh999BGZmZmuDkkYgETCIgwIRqORd999l9LSUu6++26GDh3q6pAE4bqi0Wi49dZbSUlJ4bPPPuPw4cOuDkkYYERpfqHfM5lMvPPOO9TX13PvvfcSERHh6pAE4bqkVqu5+eab0ev1bNiwAUmSGDt2rKvDEgYIkbAI/ZrdbueTTz6hpqaG+++/n7CwMFeHJAjXNZVKxY033oiiKGzYsAFfX18SEhJcHZYwAIguIaHfan5DzMnJYfXq1SJZEQQ3IUkSCxcuJCEhgY8//piSkhJXhyQMACJhEfqtnTt3cvToUZYuXcrgwYNdHY4gCFdQq9WsXLmSkJAQ3n//fWpqalwdktDPiYRF6JeOHTvGtm3bmDVrlpi6LAhuSqfTcfvtt6NWq3n//ffFKs/CNREJi9DvXLhwgfXr1zN69GhmzJjh6nAEQWiHj48Pd955J/X19Xz88cfYbDZXhyT0UyJhEfqVsrIyPvroI+Lj41myZIkoty8I/UBISAi33XYb+fn5fPXVVyiK4uqQhH7omhKWtWvXIkkSP/vZz5zbfve735GYmIiXlxcBAQHMnTuXAwcOdHiuzz77jJEjR6LX6xk5ciTr1q27ltCEAai+vp73338ff39/Vq1ahVqtdnVIgiB0Unx8PDfffDPHjh1j+/btrg5H6Ie6nbAcOnSI1157jdTU1Bbbhw0bxksvvURmZia7d+8mPj6e+fPnU15eftVz7du3j9WrV3PXXXdx7Ngx7rrrLlatWtWpREe4PpjNZt5//31kWeaOO+7AYDC4OiRBELooNTWVOXPmsGPHDo4ePerqcIR+RlK60TbX0NDAmDFjePnll/nDH/5AWloazz//fJv71tXV4efnx9atW5kzZ06b+6xevZq6ujq++eYb57aFCxcSEBDABx980KmYmp+ntrYWX1/frr4kwY3Z7XY+/PBD8vLyuP/++wkPD3d1SIIgdJOiKHz11VdkZGSwZs0ahgwZ4uqQBBfr7PW7Wy0sDz/8MIsXL2bu3Lnt7mexWHjttdfw8/Nj1KhRV91v3759zJ8/v8W2BQsWsHfv3qseYzabqaura3ETBh5FUdi4cSPnzp1j1apVIlkRhH5OkiQWL17M4MGD+eijjygtLXV1SEI/0eWE5cMPPyQ9PZ21a9dedZ8NGzbg7e2NwWDgueeeY8uWLQQHB191/9LS0lZFv8LCwtr9Q167di1+fn7OW0xMTFdfitAP7NmzhyNHjnDTTTeJ9YEEYYBQq9XceuutBAYG8v7771NbW+vqkIR+oEsJS0FBAY8++ijvvfdeu2MIZs2aRUZGBnv37mXhwoWsWrWKsrKyds/9w9keiqK0OwPkqaeeora21nkrKCjoyksR+oHs7Gy2bt3KjBkzGDNmjKvDEQShB+n1eu644w4kSeJf//oXVqvV1SEJbq5LCcuRI0coKytj7NixaDQaNBoNO3bs4IUXXkCj0WC32wHw8vJi6NChTJo0iTfffBONRsObb7551fOGh4e3ak0pKytrt9S6Xq/H19e3xU0YOGpqavjyyy8ZOXIks2bNcnU4giD0Al9fX+644w4qKirYsmWLq8MR3FyXEpY5c+aQmZlJRkaG8zZu3DjWrFlDRkbGVaeZKoqC2Wy+6nknT57c6o918+bNTJkypSvhCQOELMusW7cOnU7HTTfdJGqtCMIAFh4ezvz58zl48CBnzpxxdTiCG+vSas0+Pj4kJye32Obl5UVQUBDJyck0Njby3//93yxdupSIiAgqKyt5+eWXKSws5NZbb3Uec/fddxMVFeUcB/Poo48yY8YMnn32WW6++WbWr1/P1q1b2b17dw+8RKG/2b17N3l5edx77714eHi4OhxBEHrZhAkTOHfuHOvXr+cnP/kJ3t7erg5JcEM9WulWrVZz6tQpbrnlFoYNG8aSJUsoLy9n165dJCUlOffLz89vsXrnlClT+PDDD3nrrbdITU3l7bff5qOPPmLixIk9GZ7QDxQWFrJ9+3amT59OfHy8q8MRBKEPSJLEzTffDMAXX3whKuEKbepWHRZ3JOqw9H9ms5lXXnkFT09P7r//flHJVhCuM2fPnuX9999n4cKFTJo0ydXhCH2kV+uwCEJv+Oabb2hoaGDFihUiWRGE61BCQgITJ05ky5YtXLx40dXhCG5GJCyCW8jKyiIjI4NFixYRFBTk6nAEQXCRuXPnEhQUxGeffSamOgstiIRFcLmamho2bNjAyJEjSUtLc3U4giC4kFar5ZZbbqGyslJMdRZaEAmL4FJiCrMgCD8UFhYmpjoLrYiERXCp5inMK1asEFOYBUFwmjBhAgkJCaxfv56GhgZXhyO4AZGwCC7TPIV52rRpYgqzIAgtiKnOwg+JhEVwCbPZzGeffUZ4eLgovS8IQpu8vb1ZtmwZ586d48CBA64OR3AxkbAILvHtt9/S0NDALbfcIqYwC4JwVWKqs9BMJCxCn8vLy+Po0aMsWLBATGEWBKFDc+fOJSAggI0bN4quoeuYSFiEPiXLMt9++y2RkZGMGTPG1eEIgtAPaLVaFi5cSF5eHtnZ2a4OR3ARkbAIfSojI4OSkhIWLlyISiX+/ARB6JyhQ4cybNgwNm/eLArKXafEFUPoMyaTie+++46UlBRiY2NdHY4gCP3MggULqK+vZ+/eva4ORXABkbAIfWbnzp1YLBbmzp3r6lAEQeiHgoKCmDRpErt376a2ttbV4Qh9TCQsQp+orKxk//79TJ06FT8/P1eHIwhCPzVjxgx0Oh1bt251dShCHxMJi9AnNm/ejI+PD1OmTHF1KIIg9GMGg4E5c+aQmZlJQUGBq8MR+pBIWIRed+7cOU6fPs28efPQ6XSuDkcQhH4uLS2N8PBwvvnmG2RZdnU4Qh8RCYvQq+x2O5s2bSI2NpakpCRXhyMIwgCgUqm48cYbKS4u5vjx464OR+gjImERetXhw4cpLy/nxhtvFCsxC4LQY+Li4khKSmLr1q2YzWZXhyP0AZGwCL3GaDSybds2xowZQ0REhKvDEQRhgJk3bx4mk4ldu3a5OhShD4iEReg127ZtQ1EUZs+e7epQBEEYgPz9/Zk6dSr79u2jqqrK1eEIvUwkLEKvuHjxIocPH2bmzJl4e3u7OhxBEAaoqVOn4uXlxZYtW1wditDLRMIi9DhFUdi0aRMBAQFMmDDB1eEIgjCA6XQ65s2bx8mTJ7lw4YKrwxF6kUhYhB6Xl5fHhQsXmD9/PhqNxtXhCIIwwCUnJxMdHc22bdtcHYrQi0TCIvS4vXv3EhoayvDhw10diiAI1wFJkpg2bRoFBQXk5+e7Ohyhl4iERehRZWVlnDlzhsmTJ4tpzIIg9Jlhw4YRFBQkFkYcwER7vRuz1ZqxVzaBWoWklpxfJZUEGglJpQK15NjW/LjKtUnCvn378PHxISUlxaVxCIJwfVGpVEyZMoWvvvqKyspKgoKCXB2S0MNEwuKmFEWh4u+HsdV0tey0AmqQVI4bLb4qju+bkx61hCTZQatDUquQ1CrQqJDUamS1GjQaJI0alVbteFzjSIgkjQSq5iSqOWFS0WAxcvzYMaanTMZ2oR7bFY/xg6Tq8rGXHtO4PtkSBKF/S01N5fvvv2ffvn0sWbLE1eEIPUwkLG7KUlCPrUYm8LbhaCO8UOwK2BUUu3z5e1kBm+z4+sPH7ArIMort0n5XPmazo9jtYLWj2GzOY2WbHcw2FLuFxqpqtJ4Bl5/HroAMKJe+XnFrTjMOac6jUkvEHFBTcSCry69ZkQA1jo5KleT4Xo0zsZG0l1qYtGokjQqVVo1Kp0G69FWluZRwaVRImktJUPM29eX7kka6vJ9Wdfnczv0l0Z0lCP2QVqtlwoQJ7Nq1i1mzZuHl5eXqkIQeJBIWN2U8XIzaS8EjNcQlLQ+537xB9I2LOrWvIitYmsycfnEPY5PHET9jGoosOxMnR6Ikt0y6ZAVsyuX9bPKlm+N7nN/bka12ZIsd2WpDsdqRrTKy2Ya9weI4xi6D7VIiZQdk5dJXkLq5Lpqi4nLypJYuJ06aS61EWkfCJGnVlxKnS0mTTnNFYtRGctTW9rb2b+4GFAShS8aPH8/u3bs5ePAgs2bNcnU4Qg8SCYsbUuwyTcfL8BwX0S+6SSSVxNHMDMxmM5OmTUbt6z4rMrdofboiKcImo1gdyY5ivfwYtiuSpxaPKyhWmyNxstgcN6uMbLJirzdfPt5+KVmyX06asF9uhepS7BKXWpu4lDxJzq8/THhUGtXllqZLCVRzS1Rzdx6alq1Mzi7AH7ZIqaXLLU/NSZQbjI8ShM7w9PRk9OjRHDp0iKlTp4oV4gcQkbC4IdPZGmSThOcY162/oyid39dut7N//36Sk5Px9/fvtZi6Q1JJjgu81nUT4hRFaacV6XLCdHLndhInTr+8z6VkCfvlYxzb7JcTJ6vdcd8mo5hsKNYm5/7Olib7Fd143Uye4FKrkzOBki4nUpe629CAxqM5yVE7blo1qktjoSSNBpVWdbkFqUW3nXRFgvSD5OpSAoX68n3H+CuRQAltmzRpEocOHeLYsWOMHz/e1eEIPUQkLG7IeKgATaCENsJ1/a92ixnFZkFSa0GSsFrMmGqrMVeWYKouw9pYjc6gJ2racrKzs6mpqWH16tUui9edSZLkvAC3x5xpQZvoi0bTe58IncmT/YqWpisTKfuV3XGXttsVaG5t+kHCJdscyZNitVNdegEfbRjIIJusKDbzpYRLcTyXXUGxcSmJunSTryHpUCuXxzmpQHKOecKZTDnHLqkvJTzqS+OfmhMq9eVkyplENc++01w5YPxSonQpYVL76tCGifER7iowMJARI0awd+9exo4di0olKngMBCJhcTOy2YbpVC0+c+Nd+gnSK2IQeVv/hSLbkCTQqDXovb0w+IfhNygejUcK5zZ/gKIo7N27l8GDB4sVma9RXOpoLhw+yLBJ03rtOVokT/qePbefZRBHt73B4JTZBEcmdeoYRb6itekHrVDYWiZJXJFANY9bcg40/+F2+fLgcsXmuMl2O5hlZKMVu83camwVMijORIoOEyqvyWEYhgWhDfNCE2joiR+h0IOmTp3K66+/zqlTpxg5cqSrwxF6gEhY3ExTViWKXcJzdKhL44iY2PaA25rsvVSVFNNYdI6AQSPIzc2lpKSEO++8s48jHHgihg4jJ+Mww1wdSDdpdR5MWPAfnDz4CdUXz5MwemmHx0gqCUmnBp26DyLsOkV2dK0pV3SzKTaZhgNFNBwopnHfRQA0cTpC7hyN2keMl3AXUVFRxMXFsXfvXkaMGCG6EAcA0U7mZowH89BFa9H4u98nNlNFEaXZR/D28ydu3p0Ej1vE3r17CQsLY8iQIa4OT3ATIybcindAJPs3/oXGunJXh3NNHHWHVKj0GtReWtQ+OjQBBvwXDiHqN1MJfTiNwNXDsZVaKPnvA1S8e4KqDaddHbZwyZQpUygsLKSgoMDVoQg9QCQsbsReZ8Gcb8JzQpyrQ2mh9lw6h196nLLDm4mbugivwaNR6z0oKyvj7NmzTJkyRXx66SFqtYZ9n33Isa3fuDqUaxIRP45x8x/m6LbXsNssrg6nV0gaFboYHzxHh+IzJQYAU3YVxt1lmM5Uuzg6ASAhIYHg4GD27Nnj6lCEHiASFjdiPFYGEngmB7s6lBY8gyPwDgnHJz4Jj4ghNORlkbvxTfbu3YuPjw9JSZ0bryB0bOLyVUy+5TZMDQ3s/+xDjmxc7+qQuk2j0TMo5QbKC0+4OpRe5zs/jtBHRhP+K8eMlIp/ZGEprHdxVEJzuf7Tp09TUVHh6nCEayQSFjdiPJiHR4I3Kk+tq0NpQesfQeLqX2Ktr+Lc53+lNv8MNklNVlYWEyZMQKMRQ6F62sRltzLplttAUdj/+Ucc76ctLrLdglrdw6N73ZAkSegivdH4GzAkBwBQ9cVpar/Lo+T5AxT+ahem8zWOWVpCn0pNTcXDw4OjR4+6OhThGomExU1Yy4xYy2U8x8e6OpSrCh2/kKErHiVq+gpyq5qw2WxikcNeNnbxMiatWE1jTY2rQ+kWU2MtHt7X1yJ0QWuSCFg9HFtFE/U787BXWQGoeD2Toqd2uzi6649Go2HkyJFkZWWJhLGfEx+N3YQxvRRJp2AYHujqUDqlTPIjxLva7QrFCe7F1FiHwbtzf9PHdr7F0LRFePmG9XJUvUuSJLxGh+KZGuKo0idB46FSaj4/B0DZ20exlZjxGhuO75w4sQRDH0hJSeHIkSMUFBQQG+u+HwqF9omExQ0osoLxSBGeyUEurcjaWUajkXPnzjEuytvVobBhwwYef/xxZFnmySef5MEHH2zx+MGDB7nvvvswm83cfffd/Pa3vwVgy5Yt/PKXv8RqtTJ//nyee+45AN544w2effZZzp07R319Pd7ern+NAOX5ua4OoVtsVjOnj3yK1dKESmp/6rIkqcg/vQtzYy0AUQkTCInqvy14VyYi3hMiMBdV0XSgCsupBgDqvy/AcrEOv1mD0EZ6Y681Yy1uQD/EH5VBvDX3pNjYWHx8fMjKyhIJSz8m/ivcgCW/Dns9eI6LcnUonXLq1CkUWWbMpCkujcNms/HYY4+xbds2fH19GTNmDCtWrCAw8PIn+ocffpgPPviAkSNHMnnyZFasWMHIkSN58MEH2bFjB/Hx8fzkJz9h8+bNzJ8/n4kTJ7J582a3WjRNkWX8w/tnUb6E0Teh0RowePp36ThZlik4s5OM7W8C4BscTfzIef26YmnQ8iTkxXYq/5WF+VQdHmkhNJ0up+xERov9DMmBBN0xEsVsR+Uh3qJ7gkqlIikpiczMTBYuXNiv/46uZ+K/wQ0YDxej9lbQxfu5OpROycrKIsxHR3jSJJfGcfDgQZKSkoiKciR6ixYtYtOmTdx+++0AFBcXY7PZSE1NBeCOO+7gq6++IjQ0FB8fH+Lj4wGYPXs2n3/+OfPnz3fLMTkl589QV17m6jC6xdsvvFvHqVQq4hJvIC7xBgCqLp7h+O63QVbQGbwYMmoxeg+fngu0j6h0akLuHeW8r9iHUfXJacx51QQsGUb93iJMWVUUPb0LZAmf2TH4zY93XcADSHJyMvv37yc3N5fBgwe7OhyhG0TC4mKKTcZ4vBzvSVH9YjXchoYGcnJymBjj6+pQKC4udiYrANHR0RQVFbX7+I4dOwgJCaGhoYHMzEySkpJYv349tbW1fRp7RywmI5nfbcZsbERSqYhLSXN1SC4VGDaMwDBHDWBTYxVnj36JzWIClcTwscvw8OofY79+SFKrCLpthPO+PiEAc04ttrImar++gGyyuTC6gSUqKgp/f3+ysrJEwtJPiYTFxUynq1EsEp5juvdJtK9lZ2cjSRJDwv1dHUqbI/6vLGB3tcclSeK9997joYcewm63M23aNBobG3s11q46uWsHnv7+jF28zNWhuB2DVyDJU9YAUF1+mqKzhxmaNt/FUfUMlU6Nx/BAlCEyDUcKaDxQjDpQi+809yom2R9JkkRycjJHjhxh0aJFohxDPyQ68lzMeLgAbbCENrx/rPyamZlJlL8H8VMXuzoUoqKiWrSoFBYWtliAsb3Hp02bxp49e9i/fz9paWkMHTq07wLvwLa3X0Oj0zJi6g2uDsXt5WfvJzphoqvD6HGSRkX4wxPwSAmm7us8Gg6UoNhl6ncWIptsWC82Ur3hHLLR6upQ+5Xk5GSampq4cOGCq0MRukEkLC4km2w0nanDc3z/+PRUU1NDQUEBcX5atH6uXZwRYMKECWRlZVFUVER9fT0bN25kwYIFzscjIyNRq9UcP34cm83GBx98wE033QRAWZljTEhDQwMvvvgiDzzwgEteQ1sM3t4kzZzr6jD6BUW2YfDqH2O/ukrSqghcmYh+pC81685R9Js91G7MoX57IRdfSadxdwmNhy66Osx+JSwsjODgYLKyslwditANImFxoabMCrCDZ5rrL/6dceLECTQaDdFusjCjRqPhz3/+M7NmzWL06NH88pe/JCgoiEWLFlFcXAzASy+9xO23387w4cNZtGiRc1Dt2rVrGTFiBOPHj+enP/0piYmJALz55ptER0dTWFjI8OHDeeKJJ/r8dal1ekxGY58/b38kqd1zleeeImlUhNyVRvCPUkDn6O5sOlUOl5ZnUnmKbo2uaO4WOnXqFFaraJ3qbyRlgJT+q6urw8/Pj9raWnx9XT8gtDPKXz4AQMi/948m7VdffRVvvYZ5I0IJnXiTq8MZkD78zycICI9i2OTpDEob4+pw3N6xnW8zasa9rg6jT9R8c56GHY5E3HNCOMaDpXhPjcRa1oD31Gg8Eq+visLdVVFRwUsvvcSqVasYOXKkq8MR6Pz1W7SwuIit1ow53+x2KzNfTWVlJSUlJUSqGwkZs6DjA4RuiUsZzYKfPCqSlU4YIJ+1Oq0ppxwATYwen+mO2W8Ne4oxn62j8u1sFNnx85Atdkxnqil95RCNRy62+DnZqk3X3c/th4KDg4mIiBDdQv2QaE90kaaMMlCDh5utzHw1WVlZ6HQ6onx1SFqdq8MZuNx/ZrvbqC4/jW9gtKvD6DP+s4fRcKgA/4UJaEM8ifjNRCz59Vjy66jfUUjx/+xFH+2P6VSV85jq3DPUbj+HPsYflUZH48FSfObG4jMjGkklIWmuz8+sycnJbNu2DZPJhMHgHl3cQsdEwuIixkN5eAzz7RcluBVFITMzk+HDh5NTXcjFTRudj3l6ejFo2HACQsNaTCkWhN5WeuEoQ0YtabVdlmXOHFlHaHQagRFDnNvrqwtRaz3w7KeLMXokBuKReLnejNpHh0dSEIYRgciyTOOuYkynqlD5afBfOATDyCCsRQ3U7c/FXFKDrdqMCi31W/Op35oPWhURv5qA2su9VofvC0lJSWzZsoXTp08zatSojg8Q3IL7Xy0HIGtpI9YKBd9FMa4OpVPKy8upqKhgQtJgEsbcQMCgy//gtTXV5J46xYmM9BbHaDVaouMHEREXj1rUO+g0tUaLxWxCpxef+jpitZhbVbtVFIVDm15g+ISbKS/IIPfk96jVamxWC56+wUgqCZPRUSRQQoVao2X4uGVodf2jrEBbJJVEwOIhaLz1WArrCbw90VmEUj/Yj5DBjv/X7PQNDPabRuOBEpqOV4BVxny+xrFI43XG39+fmJgYsrOzRcLSj4griQsY00tR6RUMwwJcHUqn5ObmolKpSEgYTlHmDixNRsJGTgbAzz+AUZMmtzrGZGoi79w5Nnz0L25ec3dfh9xvRQ5LpDA7k8Gjx7s6lH7pxL5/MWLirfgGRuEfNKjD/U3GWk4d/JyUaXf1QXS9y2dmx91jhiH+6OP9KDq+23F/qH8vR+W+hg4dyt69e5FlWawt1E9c029p7dq1SJLEz372MwCsVitPPvkkKSkpeHl5ERkZyd133+2cYno1b7/9trMC6ZU3k8l0LeG5JUVWMKYX4ZES0m/6j/Py8oiKiiIgeghe/kEExid1eIzB4MHw5BSCgq+/T2/XIjIhkfOHD9DUUOfqUNyeqaGGYzvf4nT6Ouqq8gFoqq/BN7Dzi4gaPP2wWc0tti1fvpyAgABWrlzZ5jHvv/8+ycnJJCUl8cc//tG5/c4772TUqFGkpKTwk5/8BFmWWxz3i1/8guBg141Zs5hqkO02JLVEyI9TQS1R9H+HqfzsDLaqgfde25G4uDjMZrOzJpPg/rp9xTx06BCvvfaac2E5AKPRSHp6Os888wzp6el8/vnnnDlzhqVLl3Z4Pl9fX0pKSlrcBuJgKEtuLfYGCc9xka4OpVMURSEvL4+4OMdsJpvZjNaz89PGh6eOYvPnn/RWeAOOSqNh1n0/Zv9nH1Oen+vqcNzaxEU/Y9SM+4geOoWygkyO7Xwbm7XrSyzoPb2ory5x3n/kkUd4991329y3oqKCZ555hl27dpGZmcmOHTs4ffo0AC+//DLHjh0jMzOTqqoq1q9f7zwuOzub0tLSLsfWk4aPWsbJ9K84cfgzztfsomjcEbxGh2DMrKTkL4cxna1GNtmwFNRfFzOJoqKiUKvV5OXluToUoZO6lbA0NDSwZs0aXn/9dQICLndr+Pn5sWXLFlatWsXw4cOZNGkSL774IkeOHCE/P7/dc0qSRHh4eIvbQGQ8XITaB3Rx/aNWTFVVFQ0NDcTGxnbr+IDgELy8vXs4qoFNo9Ex654HOblrG8VnTrk6HLfn5RvG0FGLGTXjXiYv6XqhP0uTGUm63Boya9YsfHzaXgn6woULjBw5koCAAFQqFTNnzmTdunUAzvoRNpuNpqamFoPQn3jiCf7nf/6ny7H1JA8vb5LGLyenVM/y2x9n1aPP8l7OeqJ+PQFtnC8Vb2ZR/Lt9/OjmuwnxDWJs6ugWx2/ZsoW0tDSSkpL4+c9/7tx+7733MnjwYNLS0khLS+uwRd1daLVaoqKiRMLSj3QrYXn44YdZvHgxc+d2XD68trYWSZLw9/dvd7+Ghgbi4uKIjo5myZIlHD16tDuhuTXFKmPMqsRzbFS/mVHT/M8cGxuLzdSIStO1GQXnT2UTNyShN0Ib0PKyMgAIFavK9qqLeRn4BAbj7d+5bqShQ4eSmZlJUVERZrOZjRs3tlivauXKlYSFheHt7e1sWf7oo48YN25ct5P+nmSz2Xjsscf4/vvvOZp+gj//+c/UNNQSdl8y3nNj8Z0Xx53338U/H/gr1vImyt4+gbW0EVmWefDBB/niiy84ceIEJpOJzZs3O8/7wgsvkJGRQUZGBpGR/aP1GBzdQnl5eddFi9JA0OWE5cMPPyQ9PZ21a9d2uK/JZOJXv/oVd9xxR7vV6xITE3n77bf58ssv+eCDDzAYDEydOpWzZ89e9Riz2UxdXV2Lm7szna7qVyszgyNhCQ8Px2AwUHpiF+EjWw+wbU9IRCSVF13bFN7fHPn6C+rKypix5j40GlHzprfIssyFrG8Yktp6avTVBAYG8te//pVly5Yxe/ZsRowY0WLV308//ZSSkhIUReG7776jsbGRF154gSeffLI3XkKXHTx4kKSkJKKiojiX+U9uWnoTmzZtQtKo8J8bh++cWOb9dAWJj0xDE6DHnF/HxefTyX5hF14aD6L9HO9ds2fP5vPPP3fxq7l2cXFxNDY2UllZ6epQhE7oUsJSUFDAo48+ynvvvdfh+BKr1cptt92GLMu8/PLL7e47adIk54C16dOn8/HHHzNs2DBefPHFqx6zdu1a/Pz8nLeYGPefImw8lI82RIU21NPVoXTaleNXmurr8ArsWrIVEBRMXb37J5PuwGQ08v3brxE9IomU2fNdHc6Al73vXyRPvafLxy1btoxDhw6xZ88eIiMjW630rdPpWLZsGV988QUXLlzg3LlzjBgxgvj4eKqrq1uM++trxcXFREVFYbeZMXiGEBc/tEULUTNJJaHy1BL9m4n43zIUP4sH9RW1bPvFh+T9114++us/yc++gKWkEcVi5xe/+AWjRo3iP//zP/tVa0V0dDSSJIluoX6iSwnLkSNHKCsrY+zYsWg0GjQaDTt27OCFF15Ao9Fgt9sBR7KyatUqcnJy2LJlS5fX9lGpVIwfP77dFpannnqK2tpa562goKBLz9HX5CYbTWcb8Bzv+mbhzqqtraWmpsaZsHSHJEk01jewb+smSvNy+tWbWV8qPHmC/Z/+i+m330PYYNGF1ttqK/NApcbHv+vdF82zSkpLS/noo4+4/fbbsdls5ObmAmC32/n6669JTEwkJSWFixcvkpubS25uLgEBARw/frwnX0qXKIqCbLeTvucthqcuB2i3e1pSq/AeH0HMExN479MP+G36y9zy/r8TpPJFLjVR9td0HglbSXZ2Nvv27ePo0aNXHbDsjgwGA+Hh4SJh6Se6VIdlzpw5ZGZmtth23333kZiYyJNPPolarXYmK2fPnmXbtm0EBXW9qqSiKGRkZDhX1m2LXq9Hr9d3+dyu0pRVATJ4juofKzMDzoHSsbGxKDYrdHPczcKVq7BYzJw9cYJzp06iALKsoFJJBAQGMSQpGQ/P/lu4qyecO7yfG+5+0NVhXDey933GxEU/a/OxBQsWkJ6eTmNjI9HR0axbt47//M//5I033iAyMpKHH36YEydOoFar+dOf/kRgYCBms5nbb7+dhoYGFEVhxowZPPTQQ337ojrBMcg0l6Cw29F7GCgsLGTixM4tvjpz3g3sO3oQgPfeew/1e1sBCNMHYjpRhWdKMGvWrGHXrl3cc0/XW65cJS4ujpMnT7o6DKETupSw+Pj4kJyc3GKbl5cXQUFBJCcnY7PZWLlyJenp6WzYsAG73e6cyhcYGIhO5+iPv/vuu4mKinKOg/n973/PpEmTSEhIoK6uzjmA629/+1tPvEa3YDyUhz5Wj9qv/yRZeXl5BAcH4+3tTfHhjQQNGd3xQVeh0+lJGt16Qb+ykmKOH9iHxWxBAhTAw9OTsdNn9puByT1B79F/ugkHgqiEcRz9/jViEqcQGt2yi2bTpk2t9t+48fJyFJ980nqavl6vZ9++fR0+b0VFRTei7TkTJkzgzNlz5Jw/iafPIDZu3Mhvf/vbTh1bVlZGaGgoDQ0NvPjii7zy9PPoLnpTkHEO3j9JZZiBLzZ/wrQbb+jdF9HD4uLi2L9/PzU1NR1ODhFcq0cr3RYWFvLll18CkJaW1uKxbdu2ccMNNwCOT+5XVhasqanhxz/+MaWlpfj5+TF69Gh27tzJhAkTejI8l7FfWpk54JZhrg6lS64cv9JQX0fkuKEdHNF1oRGRhEa0bJYvysth/XvvEBEVw8TZc3r8Od2S5GhZvJ6SNFeKHT6DqKETydz9QauEZSDTaDT8+c9/5ic//wUm42956je/JygoiEWLFjlbkB588EG+/vprKisriY6O5sUXX2T58uWsXbuWb7/9FoBf//rXjL7JMQB/9cxHKC++iLXRzKTwVBYXJlHyp8P4LYjDM8X9C0c2z97Kz88XCYubk5QBMqigrq4OPz8/amtruzxmprfV7yygdlMOkc9M6ReLHQI0Njbyxz/+kRUrVpCamsqZbR8xbNbqPnv+bV99QXFREWseeti5zWI2U5KfR2lBAY2N9cy+aVmfxdPbzh7ch5d/AJHDEl0dynUj99QmvH0GExx1fY4ZMpvrOHVkE6mTHRV9u5MsWwrrqdtbjLm0EbnajNRka/F45H9NQaVT90i8velvf/sbsbGx3HTTTa4O5brU2et3/7h69nPGQ/l4DPXuN8kKXB6/EhcXR3VeFv4R8X36/DMXL2Xfd1vYs+kbwNFVpFarCY2MYtSkyRzetaNP4+lt8amjOfrtVyJh6UM1F4uIT1zg6jBcRq/3JW74WHZ+uhl7Uyyps6IJjmlZMO9CRjlN9RaaGqykzolGp7v8HiZb7JS9dhz0anQxvuiHB6IO0KP21aP21qLy0fWLZAUc73PNg6YF99V/rqD9lLXMiLVcxndB/5kdBI7uoOYp42ePbmLojLbXVektKpWKqfOun4vJ3k//Rdjgnu9yE9oxMBqXr4l/0GDiRpWhUenZ+/k5lj46GtkmowDFZ2sw1lmISwnifHo5NpO9RcKiWGWwyKhDPQlYNAhtsIfrXsg1io2N5fDhwzQ0NOAtKnO7LZGw9DJjeimSTsEwPNDVoXTJleNXQEEa4KuZNtTWUJSbQ2XZRWS55YXMZrNxw+LeayouPX+WhupKZk65v9eeQ2jJZKxGq7++Z6Y1qys3MmJcBMkzvTm44QI2i4xaq8LDW0vqLEd9K4vRiqdvywkDai8tgWsSqVp/nrK3soj82Vgkbf98n2h+r8vPz2fkyJEujka4GpGw9CJFubQyc1JQp/6RN2zYwOOPP44syzz55JM8+GDLaa4PP/wwn3zyifPTQDOTycRDDz3Evn37UKlUvP7660ybNo17772XnTt3OvsEN27c2Kmy2RaLhdLSUsaNG4fygxVn+ytZtlN0/hz5Fy4go2CxWNHrLleRNXh6Eh4Ty5CRyWi0LZcf2PXt1118LpmaygouFhYwYvTYdvc9e2AP5w4fYPF//LJLzyFcmwvHv2Vw6kJXh+FyTY3VqNVatHoPBqd5MDitddkFm01GtrfdGuWZEoIm2JOLLx6l9I1MQlYPRxPY/xat9fPzw9/fXyQsbk4kLL3Ikl+PvQ48x3a8TknzGh/btm3D19eXMWPGsGLFCgIDL7fM3HHHHdx///3827/9W4tj//CHPzBs2DDefvttrFYrjY2XV6x94YUXWLKk86XHASorK1EUhbCwMKpyjhHQjwYlmk0mivPzKCsuwmo2X272lyA0KpYJs+ei1XZtPSQPDw/qamrwvWIGQX1NNUW5uVReLMXRgA4Slwctevv5YrfJnDx8iBHjxrd5XlmWOXtwPzc+/PM2Hxd6j7mpEQ+vgI53HOBOpx8iZXL7M/GObS0gZVb0VR/XRXgRcl8SFR+epuQvh/GdE4fvjGgkdf+a8RYWFubyaedC+0TC0ouM6cWoPBX0g/063PfKNT4AFi1axKZNm7j99tud+0ydOrXNgWHvvfcep045VvXVarXXPDWvqqoKcNTOKTq4h6Ezbr2m8/WGstISdl8akHslrUZDaEQEo8ZPwODVM33RI8eM4/uvv8I/4HLyaPD0JDI2jmEpqajUbQ8slGWZjR/966oJy56P3mP0wsVu2d22fPlytm/fzpw5c/j0009bPX611r7z58+zevVqampqmDt3Ln//+9+RJIknnniCb7/9FkmSGDZsGO+88w6enn1Xe6asIJvi83tBpUJCInxQWp89t7uqqcjD0zsYtab9gbFavYrKoga82qkhZUgIIPKX46jZlEvd5lyMmeWEPpCC2qtrHw5cKTAwkNOnT7s6DKEd7vdOOUAodpmmY2V4jo5AUnX8SaN5jY9m0dHRba7x8UM1NTVoNBp+8YtfMGbMGO677z7q6+udj3dnjY+qqioMBoPzguKOF9Rld97DtAU3trpNnDOPQSOTeyxZAfD08WXJbWtaPM+46TOJjIu/arICjoHD7VVjNjc2EJHgnrOCHnnkkXZLrN9xxx18803rhPGJJ57gd7/7HefOnePixYt8/bWjO+3pp5/m+PHjHDt2jEGDBvHqq6/2WuxXKivM5NCml2ioy2PUzAdIm3E/o2bcR0T8uD55fnekKAq5J49z/thpEtI6LgaZOisGc5ONvZ+fw9Rguep+KoOGwJuHEvrQKOzlTZT9I6snw+51gYGBVFdXO5eYEdyP+12JBgjTuRpkk4TnmIhO7d9WMtGZughWq5Xz589z4403kp6eTkREBP/7v/8LOBaIPHnyZLtrfMg2K3aLqcW2qqoqAgMDkV08fmXDhg0MHz6chIQE3njjjRaPmcxmFi1aRGJiIsnJyS0Wyjx//jzjxo1j6NChPPTQQ86f7bFjx5g0aRJpaWlMnTqVCxcu9Ppr0Bk8MJtMbT5Wntf7z99ds2bNwsfH56qPT506tdWyG4qisG/fPhYvXgw4Klp/9dVXAM5xVIqi0NDQ0GcF8nwDotDqPagtK8JsrO6T5+xry5cvJyAggJUr257J19x6GxMVyU/uvwebzUL+6VP8+c23nf8/Tz31lHP/N954g4SEBCRJoqGhAYCEsWFMWBLPse8LydpZhNxkw1LUgL3Rimy0YjpfQ/2uQsrfP0n5h6fAKmMvakD+QV0WdxYYGIiiKNTU1Lg6FOEqRMLSS5oOF6IJAG1k52YiREVFtWhRKSwsJCKi42QnODgYX19f50Vi+fLlZGRkABAREYEkSXh6erJmzRoOHTrU6vjze74g98DXnN3xMWe3O25FuWfx8/Kg9MQewhPSOhV/T2se0/P999+Tnp7Os88+6+yqavbkk09y6tQpDhw4wMsvv8y5c+eA9j/l//73vycjI4M777yTZ599ttdfR3BoGBl797T5mN1mx9xk7PUY+kplZSWBgYHOZOSHrYSPPPIIkZGRZGdntxqH1VsMXoGk3fAAsSOmsf3jtX3ynH2to9awhx9+mD+v/S1bv17Prn0H+PLDfzJh/iLuvvtuTp8+TUZGBvv37+f7778HYOLEiWzevLnVoqcanYYJSwYRZLSQ///2U/biUUr+336K/2s/Fa9nUvNtLuaLRjyG+OO/bCihj45B5dF/Rh00J+A/fJ8R3IdIWHqBbLHTdLIGz7Gxnf4kOWHCBLKysigqKqK+vp6NGzeyYEHHdUgkSWL+/PnOdUy2b9/OiBEjACgpKQEcq8du2LCBpKSk1idQFIZMv4WEmatIuMFxa7QoeKhtGKtK8Yka3slX3bOuHNPj4+PjHNPTzNPTk5kzZwKO9awSEhIoKSlp91O+JEnO7rL6+vpOJYTXytvXh9q6mjYf8w0JGVBrCHXUSvjCCy9QVFTE6NGj+fDDD/skJlmWOb7rXS7mHWfhfX/sk+fsa1e2hjU11rV4rLi4GJvNxpgx4zE11vHAj/+NM4XlGDy9WbjQMUtKo9GQkpLiTC5TUlIYNGhQq+exljZS8tJR7NsL8UoKIuShVILuHIHP7Bg8x4TiNycW3zGhaIM8HC0wObUoctdr3SiKQvW2XIreOUrBm4fJf+0guS/vJ/eFveT/fhf5a/dgqzV3+bwd8fX1Ra1Wi4TFjfWf9LcfMWVXotgkPEd3fmXm5jU+Zs2ahSzLPPHEE51e4+PZZ5/lrrvuor6+nri4ON555x0A1qxZQ0VFBXa7nRtuuIEf/ehHrZ/4B+NrzGYzjY2NxI2Yz9BRo67p53AtujKmp6CggOPHjzNmzJh2P+X/3//9HwsWLOBnP/sZ3t7eHDx4sNdfR9ywRLIzjmJuMrZKToy1Nb3+/H0pODiYqqoq55pIbbUSqlQqbrvtNv7rv/6L++67r9dj2vbB00xc/B94+/d+cupqxoZajnz3PV6+XoyYOBmDh7fz/yg0ejA15SVU5xVQZWs5DqWuro6vv/6aX/6y7an1stlOzaZcGvcVo/LXE/yjFAxD/J2P1+/Jw5LbiKS59F4igSIDNtAN8kMX0bV6NzVbc2n8rhB7nKNSrkqnQeOpRrIq2IvroUlGpe/5CroqlYqAgAAqKyt7/NxCzxAJSy8wHspHF6FBE9S1yo9Lly5l6dKlLbZduUrsD8dxNBs8eDB79rTudmhu4u2IYrcjXRo8euUMIVfq7Jgek8nE6tWr+dOf/oSXlxdGY+sulubjXn75Zf7+97+zaNEi/va3v/HYY49d9Wfak4LDIykrKiJmaMvp4SGx8ch2e7sDd/sTSZKYNGkSX3/9NUuWLOHdd9/l/vsdxfDOnj1LQoLj9X/11VckJvbNYGOfwDA8fcP65Ln6WlV5CfVVFdRVVnA24zAoMG3pMuqqSyk+f5bByaOd/0fG+gbqqhpImTqZnTt3Os+hKAr33nsv//7v/05MTEyr5zCeqKBmcymKyYbv3Fh8Z8YgaVo2zFuLjXhPj8Z/0WDnNktRA2UvHgV718bBNR4ro/G7QrzmRBMw73Irj2K1U/TqEeweEPnwuF5b5iQoKEi0sLgx0SXUw+yNVkwXjHiO7x+l+A2+IZirC533m/9Zfzigsq91ZkyPoijcc889LFq0yDng8MpP+T887oMPPmDRokUArFq1ir179/bFSyEoNBRTGwNvFUlCpVZjs1o5sm0r5cUdzwrrKwsWLODWW29l48aNREdHc+jQIRYtWkRxcTEADz74IJMnT+b48eNER0ezbt06AJ599ln+8z//kyFDhhASEuLsmnvsscdITk4mNTWV8+fP89vf/rZX4887+T2Ht7xM4sQVLVaGHygqS4soPncSbz9fkiffQELaeDx9/JBtduwlhZScvEDW3u3UleZy7sxpCs4eZfTMWa3+j5544gkCAwN5/PHHWz2HYrZT/dFptOFeRDw+Dr85ca2SFXu9BcUMuh+sQeQsSdSFHiFzfh2VH59Ck+qP/9z4y3HICqX/ykIuNRF+36heXQIgMDBQJCxuTLSw9LCmzHIAPFLdf1l1AJ/wQdSV5GAIdgywa57S7OHR/puC1WKltqgKg7cBg48nar2mR2d+XDmmx9fXl40bN7a6yD311FN4enry9NNPO7e19yk/KCiI/fv3M2nSJL777juGD++b8Tmn0w+RMKr19FFJrWXTB//Ew9uHhLQxnD56hJDIjosM9oUrxws160xrX0JCAkeOHGm1vXkcUV+Q7VbOpW9jxsrfoNX3v6qrnSGpzOg9/AkKvzww1lJbS+43m/BLGMqgiAgaCoqYs/pWvH//LGaNHwoqPvjgA958800AXnnlFTIyMlr8Xq8kW2XU0T6EPph81f9tS5FjFpEuqmXC0lzKobOlFGzVJi6+fQwpwkDYrUnO51MUhcqvzmA7VUvwnSPRx159Jd+eEBgYSE1NDXa7HfUAafkcSAbeRw8XMx7MwzDIA7W3ruOd3YBPcBT1NZerO/5wDEhbSs8Vkf19OhaLmfLCi5w7fJKTO46RvT3DeTu1O/Oa4rpyTM/o0aP55S9/6RzTU1xcTGFhIc8++ywHDx4kLS2NtLQ050X2ap/yX331VX7yk58watQoXn75Zf74x74ZhLngtjs5fOBAi23ns08ge/ky8+YVzLhpGRExsZgaGrBarl7nQugclVrL9JW/IHPPu5zY1zeDe/taYOhgaspLAZg/fz4rbl7G1t27mfGTH3Ohvpa7n/gFtghHV9hLL73E7bffzvDhw1m0aBEpKSkA/PSnPyU3N5fx48eTlpbGW2+9BcCbb75JdHQ0JVUXmfE/K3jyySevGoelsB5Jq6AO+EG9oS60sMgmG8VvHgWtish701osY1K3swDTvjL8lg7BMym4kz+d7msu5yCmNrsn0cLSg2xVJizFNgJXD3F1KJ2m1miwmC+PuG+uwdIWWZY5uSMDH39fRi2c2O55j20+yJGNexm7aEq3Y+toTM/VPr1d7VP+zJkzOXr0aLfj6S6VSoVefflN2NzUxLnsEyxYuarFfuHxgzhz7ChJ49v/2Qod0+n9GDP7xxzZ+hp2uwW1uu0PEBaTkcqSE1RdPIfN4mi1MHh70FBdhodXEENGLeqTeK3WBiRJi0Zz9UKDPxQeH8vhje/w1wd/wtBPP0Hre7mi9uu/eZpBM2YAMGnSJE6cONHqeJvNhjm3lsqvLjhm89RB0V/TWaikseCxL6CsiaB7k/BIvPp4Nsv5CjQh+tYfcJoH83cwS0ixK5S8dxzqbEQ8PKbFBz3jsTLqv8nDY0YEvpP7puXxyqnNru4WF1oTCUsPMmZcRNIoGEb2rz/04OihnNz8NqGJk6iqqmxzSmNdSRVnj55i5NRRePh1POp/1PwJZG/P6IVo+yeL7XL1zB0bvyJpbOtKq8b6OlImT+3LsAa8pMmrOLz5FSYs+KmzYnPeye1UlZ5DkiQ0OgNBEQkMHbUEvYcPjfXFmBtMRA2ZRfb+93stLpOlicPnM6hsaEQBVJIeCTv1TXUgaYgJiiTIx5+R0fFXPUeAaR/+MeCTsty5TbZYyPl2C36x4Z2Kw3y+FntJI/qkS+9ZioKkkpBUEqrB/hiGXH1ZEcWuYCkx4jW+jQVVmxOYDrqEKr46g3y+gdD7UtCGXX5fMZ2vofKjU2hTAwi8se8+AIqpze5NJCw9RFEUjIcKMCT69cqUu94UkjiRkOETKDr+HQ0NjS1bWBSF8/tOYEPpRmtJ12swDESyLOPj40NddRX7vv8OrVZH9KDBrfYbnJzKmWNHGTVluguiHJgMXv6MnHwzB799ibjkaRSe2kf08MmMnvVgm/t7+UTi5RyO0bPVeI+cP05hVRkKoFNrGTskmWl+rT/cNJmbaDRVUVBRzO6TZUwbMaHVPrW7f4NuxCo8glqWHjj/9WYGLZyLxqOTY3cUBfRqQtc4ajc1HiujOv0iaoOGsIXxSNrL72WKrNCUWY5ilTGdrqDpZBXYJPTxrZMaZ77SziSh+r1FmPeX4b90CIZhlxeitJY2UvZOJqo4D8JWJfVZVWQQU5vdnUhYeoi1pBFbtYLfza2nBvYLkoQqNAnYTd25A5ysyMZuNaPW6AgweRK+ZFnXz6kozpoc17MdG74kNzcX1XdbmLf8FtTqtv/tgsMjOHXkEE2Njag1GsqKCig8dxa71ercp+piKTfd/+O+Cn1A8PGPI2XG7ZTkHGHc/Ic7fZzd1rPFyc5fLGDVlMUd7ueh98BDH0WwXxR/3/wZdcZGZqVMxkN3OQnRRE3CkrsVQ0CKs+WocMdOIsendDpZOZ9XQ8X5aiKbbOT8zwFQS2irzdjDPLFX11FYkkHMf4xB0qqwN1qp/uQkplO1AKg8wWdmLIbhga1nCEGHLSymM9XUfHUew6QwfKZc7u6x1ZopeTMDxU9D5D2jW81K6gtiarP7EglLDzGml6IyKBgS/F0dSrc1/5OOWbgGLy8v7E11mCoLUZ8v69b5tDodFrMFvaHz/fID0YRZs9Hq9Uy4YdZVk5VmKpVExu4dWM1mwuPiGT3jBvQGx4yt/LOn8XZxfZz+ytM7hCEpCzu9f8HZHYTFdbwwYFdEBgSTX1FMbHAbXShX8ZP5t9BkbmJj+g5umXS58rUqdCZnTjbg+/nnDJo3j/KMY+i9PfGKjmvnbC1lHCtn2b1JGI+UY7nYiM1sx2dRMB7JQViLG7n4twwKf7sHRQUqO6CCwDWJeCQGgVpqf1HXS4/Za83YKpscCYwESGCvtVD2XhbqId4E3zTMeYhsslHyxlEUFKJ/NKbXaq10RKza7L5EwtIDFFmh6WgxHqkhSOr+O/GqsbERlUrlXKVZ7eGLV2gcppzuLRrn4e1JU23DdZ+wePn4Mm3BjZ3ad8qNN131sdqKCuoqKjibeYyEFNdVIb4elOWfZOych3r0nFMTJ/Dpvs1dSljA0eKiIGGyWDDodJQXX6Dw7HnGLliFBORt+Z6QlBF4RUV36byJCYEcPVPFuCmt49FFeRP276OwFDWQcyCLITNHoYvzRePvaL2pKa/CJ8APtabt7u/mmT7Vn55t+8mDNUTcmYqkvpz0lH6ZgVRuJfJnY1D7uu49w8fHx7noo+BeRMLSA8w5tdgbJTzHukcNje6yWCzo9S1H/CtWM6i13Tqfh48XxrpG/MNcNwjZZrVxes9xFFlh0KgEvIJ6t45Db0qZPJXD27Yi2/rPCrj9UcHZXYTH93xCKEkSEQGe5FeUERvc+WU7ABalTeWbo9vJySlj4eA4Rs+c53wsfuG8do68Oo1Owlx/9b8lXbQPumgfDKpKDqfvZETgVEL8I9j5j0/xCgmk6vR55v2ijeU+ALWXlrCfj0FutDlm8ykKKHDxfBYNtZWMWLqkRQtKY30VBTUHiSIFW40ZbXjXyvn3JJ1Oh8ViEd3Zbqj/Nge4kaYjRah9QBfbRl9uP2KxWNDpWk7/VMwWJF338lovPy+Mda5bjTjnyGlObs9g2IRkRt4wmgtHz7gslp5iMhoZPnqsq8MY0MryTxA1ZHKvnHtq4jQOnEnv8nGeBi+WT5zPyhlLOZ4fwjdbc7odw/m8GtZvPMex4+VMndDxh6yE8clMu2cFhZln2Pb39xk+ewpjb5qNT2wU1aVXH5yqDfNCP9gPwxB/DEMDqNeWUmzOJmn1sharOMuyzKGP/8aYex9AHaimfkf3X1tPaH4PtF4xdkxwD6KF5RopNhljViXek6P7fTbeZsJiMiPpu9fCovU2YGnq+VVVO6MoOxeDlyeDxl6uZtufu+sAjuz4Hk/v/p0UuwNjfQ0FZ3YQGptMQMgQFEXBZjFTlp+Fd0AwOkPvlX6XJAl/L19qGsrw9+5aKwtAbKQvsSt92fR9LhVVRoID21/t22yx893OfOw2GSSwNNkxm2wsXDSYQN/OVwFWqVSMXjSzxbYJKxey/e/vM/6Om/EJaL/lsrGyjOy9G5h5R+sFFvd/8iKjlz2IVqfHZ0Y8NV+cw1ZjRuPvmm6h5vfAtt4PBdcSCcs1Mp2uQrFIeI7p/wusmc3mNhIWU7cTllP7MwmPd003WWVROanzxjvv56SfxsvHdc3MPcHU0MDUxUs73lFoxWpp5OSBT5HtNnSensQOn0FJzlFys7ZdqseiJzgmgYIze4gYNL7jE16DqYnj2Zm9k4Wj53T7HNOmRPPlhrPcMCOWiNDWf9fHj5WRf7GB+lozM6dFExnhSHSbmmyo1KDvZqtpM7PJwu6X38UrOhKD59UTn4ztn9BwsQi1Ts/U1T9v9fix7z4mKmUSfkGO9Y08R4dS89U5Gg8U47egdT2ovnBlwiK4F5GwXCPjoQK0wVKLokf9VZstLE0mJJ+ujfuQZZljmw8yeNQw/CL6flZLRV4pAVeMmynMzEGj1xKT1Lr2SX8SEh3DgS3fMnFe52e7CA7Z+z8hccIt6A2XW6gS0lon0+ExvV9l2FOvxWS9thpFXgYNq1cM59utuey32PH20TFv5uUZQgUXG0hNDiY2suX/rofHtb/lH/56B+bqGsLHjSJpxtWTu5Lis2itMtNW/+yq+zTVlDNqzuWKzyq9Bs/UEBr2FeI7N77FoNy+IhIW9yUSlmsgm2w0na3Hb75rPgn0tLYSFmtOAR7zF1zliNasJgvHthwkeeYYDL5Xb662NJmpuViFxWgmemR8d0NuU/GZAmfritVipbq0kpR5rSvL9geKomCqbuTihWIunDiOh5+3q0Pql2S7rUWy0teq6st5/fvvGRnlWBTV3l5FtU5SqVQsmu9Iwr/89rxzu8lso67WjO4aW1GuxlhUzIwf397hfsXH9qJWXT2G8yf34RXUuiKv9/QYjEcraDxUgvekrs2o6gkiYXFfImG5Bk1ZlWAHj7Su90W7I4vFgq/v5U9kiiwjeRiQdJ3vEtr92XdMv3Uumh8cU5ZXwsUzRai1GkBBo9XiFxpAZf5FwgZFUlNahXegT6fK/renobIOT+/LidKZ/VkkTkm5pnP2JWN1AzovAxqdhhPbjiKhoPUyUH3sBDNXrcDDT4xh6arK0jP4Bbt2Bl+gTwgjo4K5adzs3nkCxdGyWVDSwPbt+dy6fDient3ryu2IPiCAyvIagkL8293vYu5JwuNHXvXx/K8+Jn7Fna226yK98UgOoHbjeTzTQvu8HotIWNyXSFiugfFQLvoYHRq/gVFn5IctLLYzp9EOiu3SOSYunk7WtnRCYsKIutRyYrfaKcrOY/SNk1rtr/XQcSH9NH4h/pw/cprk2WOu6TVcSD9N8uzLs2gsRjMy1/5pti1mo4ma4kpqy6qxWRwzCgIigokY3vlqx43ldeSdOI8sKyiyjCzLNNY3oDcYiEsaTHB8OJkff0PyLfNEstJNBad2kTrjPpfGkJFzkgCv3us2njwhkk/WncHDS0v8YP9eS1YAxi+fy55/fsH0+1a2u59/QDhpC1onJM0UX2/8/Nte88j/pgRK/u8AtZtzCFiacE3xdpVIWNyXSFi6yV5nwZxvJmD5sI537idaJSxFRXjMmdulc3j6e5O2YCKlZwo4+s1+ho4bQeHJHEZMb7uuRWB0CIHRjmbyquLur99RlldK6al8wgZHorpiNtCouRM4vuUgg8cl4tvBJ8JmiqJgbGikuqSS+vJaFGvzwoUtF0jR6vX4hwUQn5aAztORtJ7Ylt5uwiLbZEqz86mqcFQV9vDzYtjUFDTay/+KFqMJjUGHSqUi+4stDJo+Hs/AzsUutE2lct0MsfQL2ZgstUwbcfWp0hs2bODxxx9HlmWefPJJHnyw5VpH77//PmvXrkVRFO69915++UvHbJs77riDI0eOoNVqWbJkCU/8+vcE+uo5evQoDz30EEajkZSUFN555x202p5JYlQaDRpvb3Z/9DXTVl99qYGh0xdzYtunpMxZ1eqxxoZqrCUlBF6l5Uvtp8fnhljqv8/HZ0o0muDem7n1QyJhcV8iYekm47EykMAjJdjVofSYnpzGFz4shtChUZzbfwIJCYN3x284NouVrK2HSZ7b+fEmpWcLqSoso762gQk3T281tVylUZG6YAKHN+xh+KRkvIN8qa+po6akEmN1I9gV2lqk0cPggX9YABFjIlF79Mwb/cntGciKTNiwKJJTr96SpLs06+LkV98TlZaMd8TA+Rvra+amWkfhMhfJLy9iS+Zx5qdcvWvEZrPx2GOPsW3bNnx9fRkzZgwrVqxwLkJaUVHBM888w5EjR/Dz82Pp0qUsXbqU4cOHc/fdd/Ovf/0Lm83GvHnzyDi8h9mzZ/Pggw/y8ssvM3HiRJ599lneeustfvzjnluDavKtC9n7wVft7hMalcDZXRtabFMUhaNb/oWlvoY5T7/c7vE+M2No3FdI9bpThPyoZ5dJaI9Go0GlUomExQ2JhKWbjIfyMAz1blEAqb+7MmFRjA1I15i8qFQqhnVh/Ejq/PFkb8/o9P6yLFNwMpexi6e0aFVpK45xi6eSl3GOkpP5eHt7ExQeTExCPGp97/7+7HY7h77cjUarISA0kCETRnTquNObdhA+PAG/+IhejW+g03v44R0YTtH5/UQNad0l2dv2nTnOL5asQNXO4NODBw+SlJREVJSjtWHRokVs2rSJ2293DGy9cOECI0eOJCDAsaLxzJkzWbduHb/61a9YuNAxY0yj0ZCSkkJRUREA+fn5TJzomPE0e/ZsnnnmmR5NWOwmE4qm40R++Iyl7PrXn5h622MUnD1CwaHtDL9hGSHRHXfzqHRq/JcOo+rD05jO1WAY6t8DkXeOXq8XCYsb6t+VtFzEWm7EWibjOb6frszcBkVRWiQslox0dKP67lNNM1mWyTlyGlNDU4f7qlQqvHw8201WnPuqVQwaO4zEmaOIHjsEnyj/XklWNB46LHWmyxsUR0fSuCVTO52sFKdnExgeRcCwgfP35UoJo5ZQXpRNXVV+nz6v3W5Do1bx7q4tSNLV/0aLi4udyQpAdHS0M/EAGDp0KJmZmRQVFWE2m9m4cWOLxwHq6ur4+uuvueGGGwAYMmQImzZtAmDdunWt9r9WJWdyCBwa3+F+wZFDSJl/G7ve/h8ay0uYducvO5WsNPMYFYImTEfNupPXEG3XNZfnF9zLwGke6EPGoxeRtIpj1dIBwmZzrPnRnLDITRZUvn0/yDN59hjqK2opyLyA1WR2dtaoVCqCY8MJig3tVILiKuEJUZSeKSB2nONNWa1RU1VZhSzLnR5HUVd0kWGLZ3a8o9BpqdPuJWvPe9RVFOETFIFKJZEy7R5kuw2bzYRG6+n8/VSVnSYwdHgHZ2zJbDWTlX+awirHyuYSEpIEk4elEhnYfitZW11WV3ZtBgYG8te//pVly5ah0+kYNWoUGo2mxfH33nsv//7v/05MjCPJ/cc//sEjjzzC008/zcKFC1vs3xNKzuSRvHhWp/b1D45m5v1Pd+t5JEnCb94QKt87ia3KhCaw89V5r4VIWNyTSFi6SFEUmo4U4pEU6FyRdCBo/ud0h1LUPsF++AT7tdhmt9n515vv8tv//j2yLHP/bfewcvFyqsurnPvEx8fj6+uLSqUiMjKSjRs3ArBlyxZ++ctfYrVamT9/Ps899xzg+F0+8cQTrF+/HrVaze9//3tWrWo9QLArfAP9Obn7OA319Y7nkCRCI8PaGiZzdRI0VdXgFdz3RfcGKpVKRer0u533D3/3Ake+exVJUqH39MZubV7sDmrKCplxS+cvsCfyT3Lkwjlmjkxl9KDkLg/wjYqKatECUlhY6OzOabZs2TKWLVsGwB/+8Adn9xDAE088QWBgII8//rhz28iRI9m6dSsAu3fv5tSpU12KqSNWqxUPj76ZHakf6g+Sgul0Fd6T+6Yui0hY3JNIWLrIWtiArRb8+/nKzD/U/M+p1+ux559BHdb3BZvao6Dw//68lt379jgHJv7Hr39Ook/L2Ud79+7F2/tycTVZlnnwwQfZsWMH8fHx/OQnP2Hz5s3Mnz+fN998k7q6Os6cOYOiKFRWdn+WUjNJkph087W1jgxbMIP83UfIr61D5+1J/MwJl+rXCD0ladKdaHWeaLStP7Gf2P8hJmMNBk//Vo+ZrXZ2njxAY5MRJFCrVFjtdkbGxBIXGtdq/86YMGECWVlZFBUV4evry8aNG/ntb3/bYp+ysjJCQ0MpLS3lo48+YseOHQC88sorZGRkOJPzZuXl5YSEhGCz2Xj22Wd55JFHuhWbO1AZNGhCdTRllfZZwlJXV4cs9045BKH7xLtgFxnTi1F5KOiH+Ls6lB7VvDKpRqPBeu4C+pm9VOCqmzoamHg1FRUV+Pj4EB8fDzgGIH7++efMnz+fV199lXXr1gGORCM42D1m46i0auJnTQDAVFXPuU27kW12AgZFEz6qa10VQts8vK7eehWbOIO8U9sYPmZ5i+1ltWVsyjjAkjGTCPAJ6bFYNBoNf/7zn5k1axayLPPEE08QFBTEokWLeOONN4iMjOThhx/mxIkTqNVq/vSnPzlnEP30pz9l0KBBjB/vqOz86KOPct999/Huu+/y2muvoSgKDz74IPPmzeuxeF3BIzmc+h35yGY7Kr2615+vvr6e+kutpIL7EAlLFyiygjHjIp5p4Uiq/r0y8w8112iwWq0oivutbNzRwERwJB0zZsxAo9Hw5JNPcssttxASEkJDQwOZmZkkJSWxfv16amtrASgoKOCNN95gw4YNxMbG8vLLLxMe3nYhK1cxBPowfMkNAFSezuXk+u/Q6LQk3Dij1b7G8mqqLhRSX1zGoNkTMYgy/t3i4x+J3Wrl+K63qS0vYvqK33C2+DyZBedZM31xr9R0aZ6qfKUrW00++eSTNo+z2Wxtbn/88cdbdBH1d17jw6n/Pp/GQyX4TIvu9efz9/dvUfVbcA8iYekC8/ka5CYJzzEDb6qpc3aQsQHUvVcls7s6GpgIsGfPHiIjIyksLGT27NmMGjWKoUOH8t577/HQQw9ht9uZNm0ajY2NADQ0NBASEsLhw4d59dVXefzxx3n//ff75PV0R9DweIKGx3Phu/2c+mpbq9dv8PMhcEgMOm9PGkrKRcJyDUZOdIxl+nzd+3x1+DuCvH1ZMXG+i6NyLcVm48LWXZSUVhM16doqUneVxt+AYYQ/9d/n4j05stc/UHl6erpNi6twmUhYusB4pAiNH2ijB86FoPGL9aj9vFEu9dc2nshCO/vq1StdpTMDEyMjHf3b0dHRzJkzh4yMDIYOHcq0adPYs2cPAO+9957zQh8VFcUtt9wCwIoVK3jhhRf64qVcs8Fz2q8nYqqua5XMCN2j0UX03vo//YSpvILMzbsxyRJh0yYxbWGYS+Lwmz+Ei88foX5nIb6zurZkSFf1ZBFNoee4V7u/G1OsMk3ZVXiMiRpQFwNJr8Mwaw5es+YgSRI2b280of6uDquVKwcm1tfXs3HjRhYsuLyKdGNjo7PPuaamhp07dzJihKPuSVmZY6ppQ0MDL774Ig888ADgaIbftm0bANu3b3fu39/JSuvWJ0HoqpLDx9j19ucc2nOMxFsWM/2umxk2yDXJCoA23AuvSeHUbcnFVtFxnaZrIRIW9yRaWDrJdLoKxSLhOdq9xjhcK+nS7BNJpUKn02EPcM9m0I4GJppMJpYvdwySlGWZRx55hKSkJADWrl3Lt99+C8Cvf/1rEhMTAXjqqae4/fbbWbt2LYGBgbz99tsueW09TVFknOseCd1mt8vU111fAy9ls5nMjdupr23EKymRafcsd6vk1+/GITSdKKf8zQzCfjah1wbgioTFPYmEpZOMhwvQhqjQhnq6OpQepfbVYysoRhMT6fa1BzoamHjs2LE2j3vuueectVeuFBgY6KwGOqAoiltdZPqjU2dyOHr0BItu7FxxtP6utqCYrG37kVUahi2YwahOLhTa11R6NSEPpnHxhXQq3swg5EdpSNqeT1pEwuKeRMLSCbLJRtPZevzmD3J1KD1OFRqJNetov0hYhE5SFHDh6sT93Z79x7BYLdy+eomrQ+lViqJwdudBynKL0YaFMOGOm9Fqen/K8LXShnkRfH8KFf/IpPzVowT/eDQqXc/FbbPZkGVZJCxuSCQsndB0ohLs4JEW6upQepRcVYrl5Hk8b3QMshUJy8CgKNDO0jVCB86cy+G+O5e5OoxeY2owkrHhe6xmK1ETxzBt5sSOD3IzhiH+hDyYSvkbxyn/+xFCfjQalWfPzG50p6rfQksiYekE46E8dNE6NH59U4q6r1hPnsAw7XI9D7FC6cCgyHYklft/UnZXUyaksnXHIebOHO/qUHpUfvZ58g8dR2XQk3bTXDy9+mZdnt6iH+RHyL+NouL1Y1T+K4vgB9J6pCtUJCzuS3wO64C93oI5z4Tn+O6V3XZn6qhY7AXnnfdFC8sAIYMYwtJ9w4cNpqG22tVh9Air3c67R/bx+t/+RENlNdPuWc6U1Yv6fbLSTB/rS+BtIzCfa8CUfe1La4BIWNyZaGHpgCWvDhQJQ0JAxzv3M5r4BJo2fYt2ZDLg+AdtLqom9F+KGMNyzVTq/v3WeKa8hK9PnkAlSdySmkZFVS7RyTGuDqtXeCQFownRUr8jB4+ka5/l6MqERbbYMWVXUn+6isaCelSNVtRmR40sRQK7hxrJT49nuBfewwLRD/VH7eV+hT57S//+r+wD2ihHkThLUUOfLW3el1RBwdiLc1BHDkKn01FdPTA+WV7PFFkecEtHCB2TZZlPMw9TVFtHlL8vj06b7VxGIHDiQk7u3cDYhXe5OMre4T0phpoN51Gs9mueNeSqhMV0tpqLH5xCbbRhDdTjM8gPfYAB1aWERJEV7HUWjOVGGi/UYjniqC9lDdDjPdQfryH+6Af7ofYdWEMXriQSlg5oAgxo/MF8qgzPFPesUXItdGPH0vTtJjwvJSxms9nVIQnXSBHTmq+JIvevPrX86grWZR1DQWHxiGRWjWpdK8rTNwCrceDWlNEP8QdFwnS2Bo+RQdd0ritXru8rlqIGyt/KQh3rS/itw9AEeVx1X/9LX221Zszna2g8X0PDmWrMhy6iAAzyJXhaFIbEICR1//k77gyRsHSCITGUpqyLA/JCIEkSitWOoihiDMtAISsgWli6zWazuf2gZVmWWZeVTn5NNaHe3jw85QY0aveOuTdpw73QBGuo23wOw4jAa3qfdkULizGzHEmG8JXtJytXUhnUGIb64zXGUX3YXm/BdKqKit1FVP7zJHZPDYHz4vCeEDFgEheRsHSCfngwDfvLMB65iMqgQZEVUBQUGcfFARx/ECrJ0RSvkpz3r/xeUquQNBKSRoWkVSFpVKBxfHVlE77H5DRM33+HTm8QCcsAoChKr6wofL2w22yo3PTiX1hbxWfHM5AVmYWJI7gldVynj1Wp1FhsZnSagdll4H9zIhVvZmFML8NrbPeXELBYLEiShEbTd5dHz+RgaveVUPLnw3hMiSRo8eAOrwkXPzmDPasSa6CeoEmReE8Mx2u842YpbqBqewE1689TtbOQ0JuHYhge0O8/cIuEpRP0g/2RdFD96dneexJJQdKApHbcUOG435zgaFRIGrWjf1arRtJqkHRax1et2rGf9ooE6Mqb1pEotXqsOWnyDkWxn3S2sAzElqTrSX/r0nA3dpsdtRsNupVlmS9PZpBbVUWgpyf/PnkG2m5cTONSpnD22HaSxi7oeOd+yJAQgGGEHzXrz+AxIrDbdVksFgtarbZP3wN10T7E/Hoi9XuKqNuSR/HFRiLvS253VWqbyQZBBjwivKj9JofqrXkELhyE9+QIdJHehN8xAsvMBi5+cZbKt0+gGh5A+JoRPVpkr6+5z3+lG1Pp1UT8ZgqKxd6yxUSSnBPDFbviaG2RFef3iqyA/dJXWUGxySj2S19tMlhl5/eK7fJ2xSqDTUax2hw3y6WvVjuyxQZG8xXHKCh2UOzApa+KDbq+lowes7oAWStz/nc78JB0l16f5DiVSnJcA1USSJdbkpBo/b2E44LZ3OLUfHyr/SRUBjW+8+JQe7dufjXn1WE+X+P8mV7581GsdlQ6NSofHWovLSpvHSovLWpvLSovLSqD2pGgdeJNR7HL2GvM2Gstjt8VCo7OYC5/bX5N0qVcQJJabrv0ehyvkyv+Phx/I1f+LC//HJp/Jlz+vgcoitLuG53QPpvN6hYtLKV1NXx8PB27LDM3YTjLksZc0/lCByeTs24XjO2hAN1QwIpESp49QM2GcwSu6t5ipnV1dXh7e/dwZB1T6dX4zY5FF+VNxdsnqN1ZiH8bq1KXfnYG26GLKCoJ1RA/wu8cia3GTNWWXGq/PE/D+RrC70hEUqvQRXkT/e9pNGVVUPHRaQpfziDqwZQ232/7A5GwdJJKr4Z2FtqSXP/+5qQoVyRIlxKhlsnRFUlSc3Jkk7FW+8PeTGxjvfHxCXUkWwqXk6/mZMwuXzq/4nwu5Nb7onDF947HHfdx3LfK2Cqa8BgZhHp4YKvXUfluNnKjFclDg6S+dEFXN3etqRwJXJMN2WR3ds21IOFsRZJ0aoLWjEDtp0OxKdTvKsSSX4+9xoxstF5OTFytRWJ0OZlp6z7SpaRIAjSXfy6qxibKjh5H42VArdei1mvRGHSotGrnz8/5c1Q3/0wlUKsuJ1vN3ZnNydWViXobXZ3O7eoffFX1XCLWV2w2G6o+7A74oQ3ZGZytqCDAw4OHJs1A10OxXA+tpmofHb6z46jbmov/0gRUhq7/7KqqqggKuraBu9fCY3ggHpMjqNuSjyHGB8PQyyU1FEXBdsYxk9N/XhweIxzvmxp/PaG3DseYGEjFB6co++wsYauGA47fu2dKCOEBBkrezKLwhaNE/jgVbXDnxsq4k2v6T1i7di2//vWvefTRR3n++eexWq08/fTTbNy4kQsXLuDn58fcuXP53//9XyIjI9s912effcYzzzzD+fPnGTJkCP/93//tXH1X6BpJunxhpwvd1VGWQNgLplgNvmmtM/ueZilqoOzFo85pe1cqfe4IcqMVlY+OiKcmtHvRUxQFxWxHbrRib7AiN1qRzXYUsw3FbMdWZaLxQCllf8twHiPp1eiH+qNP8EcTaEATYEDtp3dchJvf2CUuN1QpzbdLidilr877bSRpzWOdkLkisePS+KcfjIOSm5O/Kx5v8X3LpPDysfKlJFJBtspgtaPy1qLIMrLZhtxgxmY1YrLKYL8Uc/O5r7j15qVMgUuJ1g+/SniODiVo6bBefPaus9tsqCUVcpPN0Zp35e/zh9//8PcN6GN9ujy1tqyhlo+OHcFml7lhSAJLRqb1witzGOhdvl7jwqjbnIvxWDneEyO6fHxlZSVDhw7thcg6L2jRYEwljZS/mYXfokF4jQlDMdmo2V0EtRZ8bhqM79SoVsd5poQQaLRRs+4cprRQDMMuJzu6aB+i/mM0JX86RPFrx4j79aS+fEk9otsJy6FDh3jttddITU11bjMajaSnp/PMM88watQoqqur+dnPfsbSpUs5fPjwVc+1b98+Vq9ezf/7f/+P5cuXs27dOlatWsXu3buZOLH/rXPRX+l0Onx8fKiqquqT55MbrQCtEhZFUbBdNAIQcPOQDj+hS5KEZNCgMmiuOsLeZ3o09joLssWOYpUxJPh369PXQKXYlRbJUYsuzva6Oq/s8nTelzve367QdKoSY3Z5q4TFuZ/9cksgNvnSfcd2ruxGtctwxffOVsUWXbB2ZIsd2WpDtjr+BmSb46tisaOY7WAFbArYFBIUieIv93XrZ+m3aBA+M6I7te+3p49z8mIZfgYDP5owDYO2d5vqg0PjKSw6SUz0yB4974YNG3j88ceRZZknn3ySBx98sMXjlZWV3H///Zw+fRqVSsVXX33FkCFDmD59OvX1junWRUVFrFmzhueff56jR4/y0EMPYTQaSUlJ4Z133kGr7dyYFLWvHl28Fw27c7ucsMiyTHV1tUtbWAAkjYqoB1Oo/OoCdV/nUPd1DuAoHue3pO1kpZnXhHCqDpRQ9m0OsVckLACaQAOGsWGYD13EUtyALrLvu76uRbfesRsaGlizZg2vv/46f/jDH5zb/fz82LJlS4t9X3zxRSZMmEB+fj6xsW1/an/++eeZN28eTz31FABPPfUUO3bs4Pnnn+eDDz7oTohCNwUGBro0YVHsMrYqk/O+IbF1V1F3aII90PTDJtC+4pj2KPVqS0tbLFvqKfj9bkfrT3Nrj9z98ynNrTeXBq6jkhzfN3d7XTHoXKVRIRm0jkTXS4/KoEWlVyPpVI6B7NpLA9PVKsc4pCu7x6Qrvm/uplNJFL901NHN2o6Kxno+zDiE1S4zffBgfj4jtd39e1L8+Nkc3vzPHk1YbDYbjz32GNu2bcPX15cxY8awYsUKAgMv/+8++uijrF69mjvuuAOj0ehoTQR27drl3GfatGksW7YMgAcffJCXX36ZiRMn8uyzz/LWW2/x4x//uNMxeY2PpvqTM9gbLF0ar1FbW4ssyy1idxVJrSJ42VCskyKwVTSBVoU+xqfDwcSSJBE8M4aqD05hqzGj8W/ZzB500xAK8+ooefsEMT8b02OLRvaFbiUsDz/8MIsXL2bu3LktEpa21NbWIkkS/v7+V91n3759/PznP2+xbcGCBTz//PNXPcZsNrcoclZXV9ep2IX2BQYGUlpa2ifPZb+UsFR9cApblQm53oLcZHOOJwlYkeCYxSQMSJ5jQlHsl6ryalSXp/2rVY4xOVdscyYaaumKcgCXx+1ImuYxOK7t6lDaefqtZ7PILCnFW6/n/glT8dT2/fRijd4AVmuPnvPgwYMkJSURFeX41L9o0SI2bdrE7bffDjiuAYcPH+a9994DwNPTs9U5ioqKyMnJYcYMx2Ks+fn5ztb12bNn88wzz3QpYTEMC0DSQsWbGQTenow2tPVztqX5w5o7JCzNtOFeaMO9uniM4/Xaa1snLCqdmsh7kyn66xFK/nWSyAdS+k0XYZcTlg8//JD09HQOHTrU4b4mk4lf/epX3HHHHfj6+l51v9LSUsLCWs6bDwsLa/fCuXbtWn7/+993PnChU4KCgsjOzu6Tfm5NsAdqPz32egvacE80I4NQB+jR+OnRBIkWkYFOE2DAb368q8PoVdVNDfwr/RAWu53JcfH8fMZcV4fU44qLi53JCkB0dDRFRUXO+zk5OQQHB7NmzRqys7OZNWsW//d//9eizsknn3zCLbfc4qwfNGTIEDZt2sSCBQtYt25di/N1htpHR/ADqVT+M5OLzx3Gd0E8vjd0PC6vsrISlUqFn59fl57P3UiXJogoZnubj2sCDYTelkjlO9k0Hi7Fe3zXx/q4QpcSloKCAh599FE2b96MwdD+ujpWq5XbbrsNWZZ5+eWXOzz3Dy+OHV0wn3rqKR577DHn/bq6OmJiBubiXn0pMDAQs9mM0WjEy6trWX1XeSQG4vHUhF59DkHoS4qicDA/h9N7z9NksaOWNPx40hS8+rDMe0c8dN7UGqvw8+yZVoTm7p0rXfnebbVaOXjwIC+99BKpqancfffdvPXWW/zoRz9y7vPxxx/zxz/+0Xn/H//4B4888ghPP/00Cxcu7FYRN328HxFPTaH22wvUfZuH3GDBb/GQdq8rVVVVBAQEoHaDae3XornWinyVhAXAY0QQmlHBVH15AY/hgf1iDaIu/RUcOXKEsrIyxo69PJHfbrezc+dOXnrpJcxmM2q1GqvVyqpVq8jJyeH7779vt3UFIDw8vFVrSllZWatWlyvp9fo+XevhetHcFFpZWdnrCYsgDDSSSmZC7CDmTun9WXbdNXTcbLIPb2X8jFU9cr6oqKgWLSCFhYUtJktER0czePBg0tLSALj55pvZvn278/GCggIKCwuZMmWKc9vIkSPZunUrALt37+bUqVPdik3SqPBfMhS1r57ajbnIRisBtyZeNWmpqqpyq+6g7uqohaVZ6M1DKThziIufnSXyvuS+CO2adGmAwJw5c8jMzCQjI8N5GzduHGvWrCEjI6NFsnL27Fm2bt3aqdHWkydPbjVYd/PmzS3+gIW+0fzP2lcDbwVB6Fte4dHYKyp67HwTJkwgKyuLoqIi6uvr2bhxIwsWXK6mGxERQUhICDk5jpku27dvZ8SIy0XdPv74Y2699dYWSUR5eTngGND77LPPdmn8Slt8ZsTgv2IoxvQKGnYVXnW/ysrKgZGwqFXIagnFbGt3P5WnlpDlCcinqzFmlvdRdN3XpRYWHx8fkpNbZmFeXl4EBQWRnJyMzWZj5cqVpKens2HDBux2u7PlJDAw0LmY1N13301UVBRr164FHCPIZ8yYwbPPPsvNN9/M+vXr2bp1K7t37+6J1yh0QV9PbRYEoe/ZGxtpaqjBw9v/ms+l0Wj485//zKxZs5BlmSeeeIKgoCAWLVrEG2+8QWRkJM899xy33HILVquVtLS0Vt1BL7zwQotzvvvuu7z22msoisKDDz7IvHnzrjlO7wkRWIvrqf0mB8OIILQhLQfiNk9pHggJC4CiVSFbOp5y55ESjGp4AOWfnyVmaAAqD/ct99CjkRUWFvLll18COJv/mm3bto0bbrgBcIwAv3JxtilTpvDhhx/y9NNP88wzzzBkyBA++ugjUYPFRfpyarMgDCT9Y64FTLjtEY58+QYGv0DS5t5+zedbunQpS5cubbFt48aNzu/HjRtHenp6m8ceOHCg1bbHH3+cxx9//Jrj+iH/xUNoyiqn6oMThP50XItZZc1Tml1dg6WnyBqVYzmZDkiSRNiKBIr+eIiqzbkE3+zaonntueaE5cq+yPj4+DYHYLV3TLOVK1eycuXKaw1H6AF9ObVZEIS+p9XqmXTLw5TmnWTnu//LyBtWEBzrXhWHe4OkVRN420gq3shyzI6ZcHl2jDtOab5mnVxyRO2nx29OLPWb8iiubCLivmS3nOosilwIrQQFBVFVVdWp5FMQhP4rPG4E0+96ktxTB0j/9p+uDqdPGIYG4JESQO1X57DXW5zbB8qU5suULjX5+U6LhkAD8pkaLDnuWddMJCxCK1dObRYEYWCTJIlx8+8iNH4ER9a/4epw+oT/suEgQfXnl2cfDZQpzZdJjrXMOru3VkXUL8dh89dR2c7AZFcSCYvQipgpJAjXn+jEcQQmj+HI5vdcHUqvU3tp8V82DNPJWkyXVj8eKFOamykaqVODbq8kSRJB06Oxn6rCXmvu+IA+JhIWoZWgoCBUKpUYxyII15nY2GTKMq++UO1A4jk6FE2whrrvLwBw8eJFgoODXRxVD9KqsXYwrbktXmPDUNQSdfuLeyGoayMSFqEVrVZLREQE+fn5rg5FEIQ+pNbq8I6MQbZ1/ULX30iShNfEGCx5jVRXVlNbW3vVBXr7I0mnwmbqeJbQD6kMGjzGhFK7v8Sx4robEQmL0Ka4uDjy8vLEwFtB6CpFwVZjxpxTS+PRMuq+z8eY2XOF2npb5Ihx7PzkOVeH0Sc0IZ6gSOSedbSyDKSERaVTY+/EtOa2+E2MRNNkx3S2uoejujbuWyFGcKm4uDj27t1LTU0NAQEBrg5HEPoFyQp1W/Op29qyddIaKDEoZZqLouqaIWkzqTh/ApvNikajdXU4vUrt4yhmmpeTS0hIyIBajkStV2Nv6l5LmTbSC1uQgar9xUQlus+4HpGwCG1qXkgyLy9PJCyC0Ekht4/EXmdG7W9AE6BH7W+g8J10NGr3q2nRnoSpi8ne9QWps251dSi9SnVpzZ38kkIGDxvi4mh6lkavxtrNgbOSJBE0KYKab3KwN1pRe7lH4iq6hIQ2eXp6EhoaSl5enqtDEYR+w2NkEN6TIvFIDEQb5oVKr0atVSMb+9eYkMDwOIxVZa4Oo/dpVRgxU1VXTVxcnKuj6VEavRrFIiObbFiKGjAeL6duewG133Wuq98zLRRJBtNp95ktKlpYhKuKi4vj/Pnzrg5DEPo1/+RIqj4/g73Bgtpb5+pwhCtIKomLqlqAgZewGDToKk0U/26fc5usllDZFbzGhKEJMLR7vNpHhzXYQN2JSrzGhPV2uJ0iEhbhquLi4jh06BD19fX4+Pi4OhxB6Jf0CQFIioT5Qi2eqSGuDqfTfEKiKC8+T0jkwOoqaUGCUlU1/l6++Pr6ujqaHuU1IQKVpxZNgB5NoAfqQAO2ciPlrxxHsXauPktAagi1u4tQbDKSxvUdMq6PQHBbzZ84xPRmQeg+S47jE7w2zLODPd1H3pkjVOaewtPXfQZc9gbFIlOqqiEmNMrVofQ4bbAHvjfE4DkqFF2Mj2McSnOeourcmCrP5GDUFhnzpb9hVxMJi3BVPj4+BAYGinEsgnANKnZdQIk3oA1z/xkoiqKwf8MbNBTlMuPuX+HlPbAH3NfnVlIpNRCfMNjVofQJRXaMXensuobaCC9s3lrq3WRavkhYhHY112MRBKGbJAml0YZi61qZ9L5WW1/Ozrf/h+Fps0iadYurw+kTuSfPgwTxw6+PhMW5tlAnW1gkScI7OZjGrAq3qMklEhahXbGxsVy8eJGmpiZXhyII/Y5il9H5GFCV27BVXvv/0IYNGxg+fDgJCQm88UbLhQrr6+tJS0tz3vz8/Hj++edb7LNy5UrGjRvnvP/GG2+QkJCAJEkc+fQfTL/zCQKiB/CYlR/Iy8/BU6UfUGsItcfZwtLJhAXAK8EfjdGGXGfpeOdeJhIWoV1iHIsgdF/DgRJsp+oIWD38mruEbDYbjz32GN9//z3p6ek8++yzLRYo9fHxISMjg4yMDI4ePYq/vz8333yz8/EtW7a0Wol44sSJ/PHph4gMC2HCrQ+j0rpHvY2+YK+3UNxUSUxYNFJn+0j6O7lrLSwA2ghvACwljb0RUZeIhEVoV0BAAD4+PqJbSBC6QdI5EgRdxLWPXzl48CBJSUlERUXh4+PDokWL2LRpU5v77tu3j/DwcAYNGgSA1Wrlf/7nf3j66adb7Fd58AsmzViI1tB/BgT3BHujleLXD1Mh1TM4aairw+k7l/IV09lqZ2tLR9QBemSNhLVUJCyCm5MkSYxjEYRu8koLxe4jUfHNuWs+V3FxMVFRl2ezREdHU1RU1Oa+H3/8MatXr3be/8tf/sI999zTqjyBf+xQKgrOXHNs/U3BW4coqShHlpTrZsAtgH6IH0q8L9UfnyH/ucM0ZVd2ODZFkiSkKG+qD5b0UZRXJxIWoUNxcXGUlJRgsbi+D1MQ+hNJo0Ltr8dSZ7zmc7V1YWmrK0NRFD7//HNuvdVRVr+oqIjNmzdzzz33tNo3bd7teAQEYzbWI8vuPSi4p2R8sxNjuESpVINepSUkIMjVIfUZlV5DzEOjCPm3VFR6DZXvZpP/yrEOjwscH4G6yuzygeMiYRE6NGjQIGRZFlVvBaEbVD5a1CU2Go9evKbzREVFtWhRKSwsJCIiotV+u3fvJjY21rkeWEZGBtnZ2QwaNIhp06aRmZnJokWLnPsPSZkOioJs71/LB3THgU+/xSskkJErJ1MSZiTc5k/Ry8ewd3PNnf5KP8gPjyjH2BSvS1/bo/ZxjG2yN7j2Q6tIWIQOBQcHExYWxokTJ1wdiiD0O+GrkwGo/uhMp8cNtGXChAlkZWVRVFREfX09GzduZMGCBa32+2F30OLFiykpKSE3N5fdu3eTkpLCxo0bnY9Xl+UjqVRotJeXDZDtdirzzpC9/XMOrHuFA+te4eC6Vzm47lUOrHuF71/6NY0N1d1+LX0t/0IR21/5gKjURBLGJVNdXU1J1UXS5k7EXm+h8Lkj1O8tRrF3vwWhvRlcRqORRYsWkZiYSHJyMi+++KLzsS1btpCWlkZSUhI///nPndufeOIJUlNTGTVqFLfeeitGYw+00lntGDPKyH/2IKYDpXiMDyPopo5nhal89QDYXT1TSBkgamtrFUCpra11dSgD0s6dO5U//OEPitlsdnUogtDv1O8pUgqe3Knk/u9upX5/sSJbbN06z/r165WEhARlyJAhyquvvqooiqLceOONSlFRkaIoimK325WoqCiluLi4zeNzcnKUsWPHOu+/8cYbSrCfj6JWqZSQQD9lzbJ5yv7PXlb2r/u7krVvg1J2MVeRZbnVeSzmJmXPR3/t1mvoKxUXq5Td73+p7H73c+Xk1j2KbLc7H7vy/cxWZ1ZK3s9WCp7cqRQ8uVMp+9fJLj+X1WpVEhISlMLCQqWurk4ZOnSoUllZ6Xy8sbFR2b59u6IoitLQ0KAkJiYqZ8+eVex2uxIbG6vk5OQoiqIoDz30kLJp0yZFUZQW17Jf/vKXyl/+8pcuxyXLsmKtMCqm8zXKxU9OK7nP7FYKntyp5L2UrjQeL1dke+vfbVts9Wal4MmdijGzvMsxdEZnr99iLSGhU5KSkvjuu+84ffo0KSkprg5HEPoV7ymRaCO9qNyRQ/W6s1R+ew6/KdH4TIl2lEzvpKVLl7J06dIW265sLVGpVBQWFl71+Pj4eA4fPuy8/8ADD/DAAw904ZU4aHUGNHoDDbWVePu5zxgQu8XKsW+2Y6xrxMvfl0m3LECtb73gZFZWFsOGDUOn04EOwu8YQZXmNMb0MszHymlKDcYwLBBJ27lOiCtncAHOGVy33347AJ6ensycORMALy8vEhISKCkpwdfXFx8fH+Lj4wGYPXs2n3/+OfPnz3eubaQoCg0NDZ2eei0brRiPV1BzshJbmRFNtaO7y+alwW9KJD7jwtEEe3TqXM1UnloUlWMquCuJhEXolMDAQKKiosjKyhIJiyB0gz7ej8j4NGyVTVTvzKNuRwF1OwrwGBOK/4w4tF28iLjamEX3cvCLV5ly63+4OhSaGo0c+ec6JB8fRiycQWCQ/1X3LSsr4+LFi8yaNavF9sBVwzEkBlL56Rkq/3nSse02xzaVof1LZVdmcBUUFHD8+HHGjBmDp6cnDQ0NZGZmkpSUxPr166mtvbxuzyOPPMInn3zC8OHD+fOf/+zcrsgK9noL5tPVcCmPaThUijW/HlklISkK9jBPDLG++N0UgtpPjzbCq0sF464kqSRsHhqXdwmJhEXotOTkZLZu3UpTUxMeHv3rzVUQ3IUmyIOQ5YnY5w+hfl8RtXsKMB0sQ53oS+iS4V3+9OsqGq0O37BoDqx7BUWR0Xv7MXr+GpfEovcwYJEVbrhtMaofFMf7oRMnTqDX6xk6tHX9Fc/UEDxSgqnbmkdNehlVH54GQPHWEjA3Fl20D9oIbyR1ywu/0skZXCaTidWrV/OnP/0JLy9HbZ733nuPhx56CLvdzrRp02hsvFzv5IUXXuC5P/+Fn//bI7zzp9e5beJS6s7XoMqrv+rrC1g0CM9RIah9WrcsXQvFS4up2oRfj561a0TCInRaUlISmzZt4tSpU4wePdrV4QhCv6b20uI/Nx6/mTE0ppdR+d15il85QuRPx6Px17s6vE5JnrHc+f3+T//msjhUKhVD5s0g/cvvGbd83lX3UxSFzMxMRowYgUbT9uVPkiT85sXjNy8eW7WJhr3F1O8tpnr9eaRLeYnPnFi04V5owz3RBHu0OYNr4sSJrZ77nnvuYdGiRaxcudK5fdq0aezZswdwJC+SJKEoCuYLtVTtKcJ6roZ5tlE8//bb3GgbhyrKG4/UYPRD/PFMCUbSq0GSut160llaXx0mF8+mEgmL0Gm+vr7ExcWRlZUlEhZB6CGSVo33xAg8RgRS9NJhit9MJ/o/JqDStd9S4G5UBj1NDTV4ePv3+XPLskzO1p0MmzOt3f1KSkqoqqpqMa27PZoAA/6LB+O/eDByk42qj09jqmiiZncRarPd8dwaiWAPiYw9Rzj+6i4Cgv3Z8NmXPLboxxiPlaEyaJA8NDz9x9/joTXwmyefQlEUZwtMWVkZoaGhNDQ08OKLL/Lqb54n//l08k+eJmboUAKmR7O35gtSb5xA7O8mu2wZAbVeg63G5JLnbiYSFqFLkpOT2bhxI42Njc4mTUEQrp3aV0/E/WmU/C2dix9mEX5Xar9a4yZp1kpOfP8Z427q+iDea3Vo/XckLphJ+OCYdvfLysrC09PTuWRBV6g8NATfkwQ4WkvkRiuWogZsF41Yqk08fesvuPnpNSiyzL9NXoNqexVLPn6A/1v4JIoi86e//4VhQfEkRyeiqCT+58nfcfMjt7N27Vq+/fZbAB6atIbgvTbkGAP/e/Zd8vYWonpPRVJSEq+88opL/x7UOpXLC8dJSludb/1QXV0dfn5+1NbWOkdXCz2vsbGRP/3pTyxatIjx48e7OhxBGHCaTlRS+c9svOZEEzCv6xdWV9r/6UtMWvnTPn3O2opqMjdsY9q9K9rdT5Zl/vrXv5KQkMCSJUt6PS5FUVAsMrLJhtJkQzbZkE125CYbdVkV2E9UogQbiH58HLaLRi4+nw5A4JoReKYE93p8XaEoCiVrD2KSYPBTEzs+oIs6e/0WLSxCl3h5eTFkyBCysrJEwiIIvcAjKQivudE0bi3EEOmDR5J7Xbzao/byxlhTgad/38V87KvvGHdrx108hYWF1NbWkpyc3AdRXVqDR69GpVeDX8sxSV6jQ6n7Pp+6zXmUvXMC04VaJK0K71EheCS5zzTxZvZaC3KdBX24axfJFJVuhS5LTk4mLy+vxfQ7QRB6jv+ceNQjfSn/8KRbrJLbWSmzVnJi++d99nz7PthA3NgUPL07vpBmZWXh4+NDbGxsH0TWMd/ZsfguHUJTQT26YQFE/2YigSuH9frg2e5QLI7xOsHLXLuytUhYhC5LTExErVaTnZ3t6lAEYUCSJImw25IhQEvJWxnYG62uDqlTDAZv7Lber9WhKAp7/vExsRNSiUsd3uH+drudEydOkJSUhErlPpc93ymRxD8zmYg7R3ZY68WVmhMWSevageDu85sT+g2DwUBCQgJZWVmuDkUQBiyVTk3kfWlgkSl599g1rXPTlzR+/tRXlPTa+WWrld2v/oshN84makjnWkvy8vJobGzss+6ggaZ5sG1nK//2FpGwCN2SnJxMUVERVVVVrg5FEAYsTYCBsLtSUQqMVHx5xtXhdErKzFs4uevLXjm3LMts/uPrJK2+mfCIzo+TycrKIiAgoEU1WqHzFItIWIR+bNiwYWi1WjIzM10diiAMaPrBfvgvHYL5QDl13+djqzK1WVnVXeh1Hsj23unCOr5pF2l3LScwwLvTx1itVrKzs0lKSupX08Tdia3ahCKBqgvrXvUG9+00E9yaTqcjNTWVQ4cOMXXq1KtWjRQE4dr5TIrCdLGeus151G3OQ9aC3/QYPEeHogn6/+3dd3hVZbrw/+/aNclO74WEEEoCBBJ6E1BEQBAULIDKHDn2UUeP036ea7wcZ7xez5x5z3mPM+qcscw4joBiQUFEqlIkSCgJIDUQShohdafu+vz+CGyNoSQhyd5J7s91xZi1117rfvIkrDtP9fe5gZrGiEhqis8RFHf1dVF+TLndaFcZY2I9X0bmrVPbdM2cnBxsNhsjR45s0/t6GqUUropG6vaVglLUl9ZjO2MFhxv/wRFEzErGEHL5FZZrjlXgjPT3+mKG8pQR7TZhwgT27t3r2chLCNF5om5PwzUtBftZK2UfHqVmyzlqtpzDbQR3jIHgpEiCxsZjjPX+go7DJi8g57O3GXvn461+j7O8HOvnn2NK6Y9l4gS0H+0JdHxXDgkZQ9oUh9vtJisri8GDBxMeHt6m93ZHrlo7jqI6bPnVNJyvo6GyaWVaY40Dl92F3v79OChHbAB+fYPxi/Sn7usCSvaXErFkSItp1bb8apxHKoi41ftrAknCItotMjKS1NRUdu7cSWZmpk+NvheiJ9IHmfAfGkni0Btw1TlwFNViL6yl9mw5NbuLcDTYiVk41NthYjKYcLmdbXqPo6CAkNtvRzOZqN26FV2AhYBxY7/vxik8x7n0cZw7VgpAo8PFrPS4q17z2LFjVFRUsGDB1ReV6y6Uu2mFXVe1DeVW6Ex67IW11J+uxpZ93nOey6TDZdLjnxyMcrlxVNgImRCPX0oI+hAzhih/NP33/14HT+5DyRsHKP/nYUgJJu6+Iegvdv+UfpmPO9KfoBu8P/5HEhZxXSZNmsTf/vY3Tpw4QWrqtacXCiE6ht5iRD8wDL+BYQSTSP5/7cBg9J1/0k2xcVSfO0lIYv9Wne+qrkYXEoKmaQRNm4bLaqV282b04eEYIiJIGpbKoEHRnvO/vpi4XM0333xD37596dOnT7vL4W22/GrOrzqBu8aO3uZCu8xkMUeoCWO8Ba28kaiHhmHsE9im8Tp6i5H4p0ZQuSqP+r3ncVU2orcYaTxZhXamhuj7B/tEt6Pv/HSLbikxMZE+ffqwc+dOSViE8CaHQu/n3UGRPzRs4jxyVv+dsa1IWBwlJbjr6po9ZPXBwQRNn46zrIyaLVsIu+eeNt3/7NmzFBQUsHjx4jbH7m3OqkZqs89Tuf88xgobOppWxzUnBaEPMaMPMYPWtD6KMToAXcD117tm0NHY4MAZZsaY0DSouTb3Ao4gI34+svquJCziumiaxsSJE1m5ciWFhYUybVAIL9EcoDN755/0ysZKNDRC/UI9x0x6E4orz2Zy19dTv28/ym7HGBtD0KxZlz3PEBnZ5mQFYOfOnURGRjJw4MA2v9cblFvReKScit0lqGOVAPilRxA6JwW/QWGdvmibq9aO62jTWJVLiWPN8Qosg8J8ZnaVJCziuqWlpREeHs7OnTu5++67vR2OEL1O44lK9PUKfbCpS+9ba6+lorGC3Au5JAYlUlFaQUZUBg3OBuwuO3XW8paxHj6M4/x5dP4BWMaOQTO1L+ayWjulNY1EB/m1fK2sjKNHjzJ37lyfHFvnrGik9ItTBA2OIGhUDPXflVG6+iSGajvOaH/CZyVjGRnTpfVZu+c8Cg3LqBgAXDV2jFV2AgeFdVkM1yIJi7huOp2OCRMm8MUXX1BRUdErRuML4Uuq9xXiCtcTMDKmS++bV5VHta2a2f1mo9c1tQDsKNxBvaOeQFMgQxNHoVyuZjN+HMXFBE6bdt1/tS8YkcC2ExeITm2ZsGRlZWGxWBg+fPh13aO9lMOFq9aBu9aBq67ps7vOgbPWjq2qEfvhCnQuRXl5A4EZUZStOIoxPpDI+wZjTrrybsWdFq9bUbmzCL+h4Z7upfqDZSgNzCmhXR7PlUjCIjpERkYGX331Fbt27WL27GvvnCqE6Dg2awP6cDOavmub7jOjM9lWsA2r3UqYX9Nf4jX2GmYlz+Lv3/2dsWPupnbrNjS9Dl1QEJrJjGYwdEgXg06nXfY6tbW15ObmMmXKFIzGjhvT46yy4SxvAHVxpk6tA1NiEPpAE3W5pVQeLEPV2NE3ONE5W3aFuUw6XH56tAAjwZPiMZr0VG05R/E7h9A5FVG3D8CU0PoF8TqS7VQVBqudsElNXfrK5ab8q7OYB4ejD+raVrurkYRFdAiTycSYMWPYuXMnN954IwEB3t2GXIjexBIbQu2e87hq7F3+gJmcMJkvT3/J9KTpGPVG/PR+ONwOFgxYwM6y/RyJOE5ScBK3hGei6urwT++4adfuy6z4m52djaZpjB49usPuU/bBMRr3X3lWkluvYRwQimVAKPpAE7ogE3qLEV2gsemzxYhmaN415axspPpUNbZKG9H3DPJasgJQ8U0RzjAzpr5NrTv1uRcw1DiIuCXZazFdjiQsosOMHTuWb775hj179jBlyhRvhyNErxF6UzJ1+0spWXmIhAe7dhFHTdOY3nc6G09v5NZ+txJoCqSwtpAgUxCFtYXMSJ5BsCmYrPIDTO4zuVNjsdvt7N69mxEjRnTYH03K4fIkKzHPjEQz6tAFmqjaWUT9sXLCx8fjNzgCnbltg2INYX4kPuKdLqsfctVcHGw7p2mwrXIryjaeQT8gBFOc9xch/CHfG40kui2LxUJmZibffvstDkfn7CUihGhJH2gi7MZkXPl1Xb7P0Lmac2QVZWHQGbC5bIyJHcOJyhNkl2Qzo+8MTlSeIMgUxBnrGZxtXEzuWjTA7f6+vDk5OTQ2NjJhwoQOu0fJB8cAiHx4GMZYC4YIf3RmPeE3JdLnsUwCMqPbnKz4kto951GahuXi+KfGw+XoK21E+VjrCkjCIjrYhAkTqKur48CBA94ORYhexRDuh84J7rqu+WOhtL6U1SdX43A5mJwwmRnJM/AzNA2AHRA2gIGhA4nwj2Ba0jR2Fe9iTOwYDLqObdRPiw3maEkN8P0y/EOGDCEsrGNmttRmF+M6VE7IvBT8+od2yDV9iXIrKrMKMaVHoAswopSidMNp3ImBmPt2/eDfa5GERXSoiIgI0tLS2LlzJ273ZZZkFEJ0Cn14U7LgqrR1yf2i/KMIMASQEprSYvBrSkgKA8IGAGDQGZjSZwqp4R2/sKROB41OFwBHjx6lsrKSiRMndsi1G45WUPlJHqbRMQROiO+Qa/oa28kqDFYHYRObymc7UYW+tIHoGcneDewKJGERHW7SpEmUl5dz5MgRb4ciRK9hCGtKWJwXN7zrbJqmkRKSwsmqk11yvx8rqmrgYEE1IxJDcbvdbN++nb59+3bI4pUNRyu48N5h9APDiFow0GcWTuto5d8UNhtsW7rhNK6YAMwDQr0b2BVIwiI6XGJiIgMHDmTjxo0ylkWILqLzN+A2gauLEhaAlNAUTlWfuq5xM47SUqwbN1KzaRONx4+3+n1HS6xMS4tG0zRyc3MpLi5m2rRp13yfq8aO43wdbntTy4xyK5xVNuxFtdQfuEDRO4cof+c79P1CiFviG3vodAZXjR33sUoiJsWjaRq209XoCmqJnpHsswmazBISnWLmzJm8/vrrZGVlyYwhIbqIK1jDVl5HUBfec2zsWHYV72JCfNsGujorKmjYtw9DZCRB06c3PTRP5WP9cj2W8ePQh4Ze8xqapmGz2di8eTPp6en07dsXd6MTNA3bySqcZQ1oZj31Z6w0nLWiVdrQuZqSK5dJhyvQiKHa7jkG4IzyI+L2/gSOi+uxyQo0DbYFPINtL2w8gyvcjN9g3134UxIW0SkiIyMZN24c27dvJzMzk+Bg3xvAJURPYwz3p77M2qX3DDGHoNN0VDVWNdtL6EqclZU07N2LPjycwJtvbvbXvDmlH6Z+ydR/+y3K4cQyaSLaj5bWV0qRdaqchNCmacvbtm2jsaGR4dV9Of3iTgwNrhb3dISaMEUHYIoKICA9En2Imdoj5Si7C78YC4ZI/6b1UoJNGELM1/cN6S4MGppqmgWlGXVwspqohak+naRJwiI6zZQpU8jNzWXz5s3Mnz/f2+EI0eP5RwRjPVbS5fcdGzuWjWc2MiN5xhXPcVmt1Gdnow8ObpGo/JCmaVjGj8ddV0fN5s2YkpLwS01FKcWuUxXUNDoYkxxOmMXEhYJSsr7JItPRF1OZImx8PMaYAJRLYYy1YIyzgOKyKwD7+eg4ja4SMrkPxlA/Lnx0HBVgIGxWMv4ZUd4O66quawzLyy+/jKZpPPPMM55jn3zyCTNnziQyMhJN08jJybnmdd555x00TWvx0djYdX2xouP5+/szbdo0cnNzKSgo8HY4QvR4xmA/dPVdf19N0xgWOYwDF1ouZ+CqbUo8Gg8fIfCmmwgYM6ZVYyR0FgvBt9yCZjJx7tPP+eir7xgSH8yMobGEWUwopfh82af4Y+Kmn8ym76/GEDIzmYDMaCyjYjAlBKLptC7frqA7CRgWSdJvJzR9725M9OnWFbiOhCU7O5s33nijxeZSdXV1TJo0if/4j/9o0/WCg4MpLi5u9uHn13JTK9G9jBw5kpiYGNatWyfTnIXoZPogI/pGUM6u/12LC4yjsrGSRmfTH5ru+npqtmyh8UAugTfdhGX8uBbdO61h7tePPrfPIeJoDkV7D7DjH6tw1tk59NEuzjQUMW38VIJSo5olQUqpLl9Ar7u61EDQHbSrS6i2tpb77ruPN998k5deeqnZa0uWLAHg9OnTbbqmpmnExsa2Jxzhw3Q6HbfeeivvvPMOBw8eJCMjw9shCdFj6S7uI+SqdWAI7fqxGDck3MDXeRsYW+iHZjQROGUKmuH6Rx5omsaYxfOoKasiLiWZot9nscW0mxhTOCNnjvecp5xuancWYt14GuUCzXDxw6yh8zOiCzCiCzSjD/LDGG3BmBiEMSbA51sWRJN2/SQ98cQTzJkzh+nTp7dIWNqrtraWvn374nK5yMzM5Pe//z0jRoy44vk2mw2b7fsFkqzWrh1oJlovOTmZIUOGsGnTJtLS0jCbe8mgNiG62KWND901dujihMVtt3Nk3QoMjkoCb38CrQN3SgYICgsmKKxp8P7++Eoqy+v41zvubNr/RikaD5dT+ekx3DUu/EdEYk4Kxd3gxN3oxF1rw1Vrw11vx1nVgGp0465TgAZ6BS6NoKkJhNya0qExi47V5oTl/fffZ9++fWRnZ3dYEGlpabzzzjsMGzYMq9XKK6+8wqRJk8jNzWXgwIGXfc/LL7/Miy++2GExiM51yy238Oqrr7Jjxw5uvvlmb4cjRI+kD2xKEpxVNkyJXTO5WTkcHP7yfSptlfSfPp/00MROvV99fT27G46RqovHtPECjaYwqtYex3nejinJn7AHB2OMvfamfW6bC0dhDbbTVqwbzlCztZDgWf26TfdIb9SmhOXcuXM8/fTTbNiwoUPHl4wfP57x479v1ps0aRIjR47kz3/+M3/6058u+57nnnuOZ5991vO11WolMbFzf1FE+4WFhTFp0iS++eYbRo4c2WF7fQghvqeZDbgNqmkPnIrGpt0BNZr+o33/CZ2GZtSjGTXcDa6LB8EQ4d/q2TPK6WxKVBrK6X/zfIaGJ3VGkVrYunUrLpeLGXffRsO7eZT97RCGSAORD6bjN7D1/67ozHrMKaGYU0IxRgdQ/t4R6nNKsYyI6cToxfVoU8Kyd+9eSktLGTVqlOeYy+Vi27ZtvPrqq9hsNvT669+1UqfTMWbMGE6cOHHFc8xms3QtdDM33HAD+/fvZ8OGDSxcuNDb4QjR4+jMekJmJlO16TSNp6uaDl4ae/qDz7ofLVVysXMEgJA7+hM0/sp75yiXi8Pr36ey5gL9b76DoZHJHRb/tZSWlrJ7926mT59OxJB4Gn5iRudnwNQv+LpaRvzTIzEPDKL68xMEZETLmBYf1aaE5eabb+bgwYPNji1dupS0tDR+/etfd0iyAk0jvHNychg2bFiHXE/4BpPJxPTp01m1ahX5+fn069fP2yEJ0eOETE4iZPLVWzuUUtR9W4Kyu7CMi0Nn1qOUovyz41R9mofOqMcyqnlLg3K7ObJxJRVVxaTcdDtDo7t2vIdSivXr1xMaGsq4ceMA8B8S0WHXD74lhQuv52I7WYW5fyjOC/U4Sutx1zpQdheaSY8pKRhjvEW6jbykTQlLUFAQ6enpzY5ZLBYiIiI8xysqKjh79ixFRUUAHDt2DIDY2FjPLKCf/OQnJCQk8PLLLwPw4osvMn78eAYOHIjVauVPf/oTOTk5vPbaa9dXOuFzhg0bRnZ2Nl9++SWPPvoounZMcxRCXB9N0wgcH9fiWMS8QVywO6n46BiaSUfAsCiUUhzZuJLKiiKSb7yNIbGXH1fY2Y4fP87JkydZtGgRhg6YefRjpsQgND8oX/4dyu4G18WkRANND8qlQGlo/hB0YzKBE+LRmTrmj3TROh1e66tXr2bp0qWerxctWgTACy+8wG9/+1sAzp492+xBVVVVxSOPPEJJSQkhISGMGDGCbdu2MXbs2I4OT3iZTqdj1qxZvPXWW2RnZ3v+UhJCeJ+m04i6awjnbQepWHaUgpFZXHCdpO/UWxkS771uXLvdzvr160lJSSE1NbVT7qFpGsHTk7GdqMDcPwJjQiDGWAu6AEPTTCSXG1t+NXXZRVi/zKfmqzNEPjAMc3JIp8QjWtJUD1ldx2q1EhISQnV1texb0w18/vnn5OTk8MgjjxAdHe3tcIQQNO1c3PBdGVWrj+GuUagJASTePurab+xka9asITc3l8cee4zIyEhvh4OzopHyZQdxFDcQ8S/p+Kf67oaB3UFrn9/SHi+8YsaMGYSGhvLxxx/jdDq9HY4QvZpyKxoOlVHynzupWHYUQ3gAUY8N79Rk5fPPPyc1NZWBAwfy1ltvtXh99+7dDB06lKSkJP77v/+bWbNmERkZyb333ktqairp6ek899xz35dBKX75y18yaNAgBg8ezMqVKzstdkO4H9GPjcKcHET5P7/DVWPvtHuJ70nCIrzCZDJx5513UlZWxqZNm7wdjhC9kidR+WMW5e8dQR/kR9Rjw4l+fGSndnU4nU6effZZtmzZwr59+/jDH/5ARUVFs3OeeOIJ3nzzTR577DHOnj3rmRX6k5/8hGPHjpGTk8OuXbvYsmULAG+//TZWq5Xjx49z+PBhpk2b1mnxA2hGHeH3paNpUP3lyU691/VQLjd12SWc+WsuZ988gHVHIcrdPTtWJGERXhMXF8fNN9/Mrl27yMvL83Y4QvQaSjV1/ZT834uJisVE1KPDiX5iVJeMybjUepKQkEBQUBCzZ89m/fr1nteLiopwOp2cOHECs9nMI488wueffw7ArFmzADAYDAwbNozCwkIA/vrXv/L8888DTeNRuqLrSG8xEjA6lsbDZZ1+r/aq3HiGio9PeJbjsX5+ivMfHkO5ut/ebpKwCK8aP348/fv359NPP6Wurs7b4QjR4znKGij54y7K/3kEZYOoR4YR/eRozP26bvBoUVERCQkJnq/79OnjSTwuvR4QEEB+fj7z588nJSWl2evQNO5h7dq13HjjjUDTwqZvvfUWo0ePZsGCBZSUlHRJWcwpobgbwGW1XftkL6jecx6/UdEkPZpB0kPDCVuYiiP3AsXLjnS7lhZJWIRX6XQ67rjjDlwuF5999pnssCpEJ3PXO8DZtLqtu86FPrjrF+C83O/5D9c2KS0tpaysjIkTJ5KSktLidaUUDzzwAD/96U89K5zX1tYSFRXFnj17mDlzJj//+c87uRRNTElNg0Tt52q65H5t5ZcSQv3hcpxVTQmVZUQ0kfcNwXW4gqrNZ7wcXdtIwiK8LigoiNtvv53jx4+zZ88eb4cjRI9mTgom7t9vwJQUgCHSgD6i47ZZaa2EhIRmLSYFBQXExTWtC2O329m3bx/19fWecSg/fB3gV7/6FeHh4c2SkoSEBO68804AFixYQE5OTheUBPQBTauDuO2+2cUSPbc/GHQU/r891OWUAuA/NILA6UnUbT5Hw3flXo6w9SRhET4hLS2N0aNHs379ei5cuODtcITo8RwldfgNifHKqq1jx47l0KFDFBYWUlNTwxdffMHMmTMBPGNZIiIiOHz4ME6nkxUrVjB37lwA/vd//5ecnBz+8pe/NLvmvHnz+OqrrwD4+uuvGTx4cBeV5uL37zKtRm6bE9upKhrzqnCU1uOsttFwrIKabwqp3niGqi/yqdl6jsbjlbjqHJ0SnT7IROK/jcI8IIyKlcdxVjQCEDotCd2QcC58cBRHaX2n3LujyToswmfY7XbeeOMN9Ho9Dz/8cKesZimEaFL6v3tx19mJeXa8V5KW1atX84tf/AK3282vfvUrHnnkEaZMmcKYMWNYvHgxTqeTBx98kMbGRpYsWeJZeNRgMNCvXz8slqYdmZ9++mmWLl1KRUUFixcvpri4mPDwcN555x2Sk5M7vRzK6abwN98Qdvcgz3YGtlNVVH1xAkdBA9/v0tSSLgDcNuDi3k76cCORS4djjAro8DjdjU6KfptF2D2DsIxsitNtc3Luz/vRjDoSnxyJpvfOlgOtfX7LE0H4jEtTnd966y02b97s+YtLCNHxgqYkU/7uYRqPVeKf1vULn82bN4958+Z5vrZarcyZM4e+ffsyatQoNE3ju+++a/G+K63bFB4e3mymUVdxNzbFo5n0uOocVKw4hC2vFl2AjqslK03vbX6Kq8JB9ed5RC4d3uFxOsubWlZ05u8f+zqzgdh7Uil9PZeabwoInpLY4fftSNIlJHzKpanOWVlZMtVZiE7kNzgcU5I/FSu+w1ne0K5ruOoc1O4swnHh+roU3G43q1atwmAwMG/evG61uaDjfFPZbSerKPnjt03Jir8ed/33Y1r8UsMIu2cQ5glxuA0abj89gTckEHpbf0JmpRAyux+ht/cn8sF0whcPaXb91i6wN2DAAH73u995jr/00kskJSV5pneXrjuFM9zMMds5xo8fT2ZmJpMmTaLQWYbfxDiqNpxp989BV5GERfic8ePHk5KSIlOdhehEmqYRsWQ4OrOe83/ag+1UVavepxwuancVUfL/dlH8+11UrT6J7WT1dcWSlZXlmcIcENDx3SGdSWfWo5mhblcxqrFphIVyudAFGgGI/+0EIpemYxkZQ9TtA4h/djT6xCBqdxRSdbwC/+GRBE3pQ+CEePwGhqHz+74FpLUL7K1YsYKjR4+yZs0aDh06BMDMmTP59ttvAajdUwJ51UTfmsLzLzzPiy++SE5ODvfffz9/+MMfiJjZD7fFSMlHx316pqYkLMLn/Hiqs9vtm6Pvheju9EEmop8egyHczIU3DnLhjf3YTle3WJ9DuRX2wlqqN52m6KWdVH2a53mwamYNy5jYdsdQWFjI5s2bm01h7k5MfYJIeHEylvFN40ICRkcR9+8TiHp0OG69RsWm5lOHDeF+xP9rOuH3puE6V0Phf+2h+gqrz7Z2gb3hw4djMBi49957WbNmDQBjxoxpmlnlVlSsysM4Khr/9Ag0TaOmpmkKdk1NDXFxcejMemLvHAT5Vur2nO+sb9V1kzEswicFBwczf/58li9fzqZNm5gxY4a3QxKiR9JbjEQ/NYaGAxeo+vw4F/73AOiaBoRqRh3K7m4aa+ECdIqAzCiCpyVjiPSn+P9sxy8tqt2DNauqqlixYgVxcXGdvpR+Z2o4WkHdrvME35pM8NSmcSA6PwMhM/pSs+40Df1C8R8a4Tlf0zQChkeRODCM8nX5WD8/RdW+88TdNQhTfKDnvNYssPfj17du3er52lVjx21zQUwA0XcMRNM0/vM//5OZM2fyzDPPEBgYyO7duwHwGxSGLiOS8rWn8E8LRx9k6vhv1HWShEX4rEGDBnHrrbeybt06QkNDGTt2rLdDEqJH0nQaAZnR+A+Pwn7Wir2gFmd5ParRgc5iRh9kxpQUhKlPIJpR//37THpcte1b4bWhoYFly5ZhMBhYvHhxt50VqFxuKj86ginRn6ApfZq9Fjy5D3VnrFx4/yhxT2RijLU0e13nbyBqwUBso2Io+eg45/+8HzUgBP9wf/zC/WjMr8JlteOsaETnb0AphU73fcfI1Rbgc9tdFP69qXso/l+Gohmb3vf666/zl7/8hdmzZ/Paa6/x7LPPesbGxMwbQMF/7eHCp3nELhnS4tre1j1/QkSvMW7cOCorK1m3bh3BwcGkpaV5OyQheixNp2FODmn1fkLmAVE05JaglGrTQFmn08kHH3xATU0NDz30EIGBgdd+k49qOFCGu9ZN6IODW3wPNJ1G7MI0zr22n6I3DhCzMBW/1JYzssx9g0l6ZiTWbwqpPl5Jzckq6nPsBJ60k7/7KCX/mQ3AsU27yUgcyun6LNxmPc7GCvJz8yh87zABkf6c2nmYSIIo/+QEtQcuoDnd6Mx6DCHfr2a8YsUK/vSnPwFwzz338Nprr3le01uMRM7tT+UHx2g4WuGV2WNXI2NYhM+bMWMGaWlpfPTRRy32ExFCeE9ARjTuBmg41PrN/5RSrF69mnPnzrF48eIu2aSws7jtLqrX52FK8scUZ7nsOTqznj6PDEcfb6Hs799RtuYkytlyXJ6m1xEyJZGkh4aT8osx9H1xErP/+q+cdBZhmxOO8Y5EtpbuY95DdxM+MYHwoZH0H5qCwWjgwJGDlO8uZMUH7zOxfhDWI+VYxsUS/8wo0DVPoiIiIti1axcAmzdvJjU1tdnrAZlRqOQgSj8/iXL51gBcSViEz9PpdCxYsIDY2FiWL19OZWWlt0MSQgCm5GDMAwKpXHkUZ1Vjq97z1VdfceDAAebPn0/fvn07OcLO46q1U/raXlw1LkLnpV71XH2giYR/HUbQ7H7UZxVz7rX9rZoKbrL48V//89/c+uB8Jt0/nV/95v8jeU46i/7rMepH+BF95yD+uvwtfvHp75j5j3/h9gfv5saX7yH5uXG8+u27JGcOpLKykj59+nhaVf7617/y+OOPk5GRweuvv84f//jHZvfUNI2Y2/qjL2ukbm/XbCDZWrLSreg26urqePvtt9E0jQcffLDbTX8Uoidy1zso+b/fogsyEv3EaHQm/RXP3bdvH6tXr2b69OnccMMNXRhlx1FON43HKqj8+CjKDVEPZmBKDGr1++2FtRQvO4xW4yD89v5YRnlne4RrKV5+BFt+NX3/fVynx9fa57ckLKJbKS8v56233iI6OpolS5Z024F6QvQktrNWLvw1F2O0mahHRqLzb/l7mZeXx7Jlyxg1ahRz5szp0IegcitUoxN3vRNXvQPV6EK53OBSTdOF3Qo0rWk2k0HX9FmnoTPp0cx69KHmqyZayq2w5VdTt6eIxkPlKAcYY81EPJCBIbTtu127bS5KPz2Bc/8F9EPCCZ+aiCkpyKcSl7p956lceZyElyahGTq3M0YSFtFjnTt3jn/84x+kpaWxYMGCZqPmhRDe0fBdOeX/PIxfWjCRD2Q0e62kpIS//e1v9O3bl0WLFqHXXzk5uBJXtY36nAs4KxtwltXgqrbhbnCi7KAcwHU8yTR/jeifjmy2h49SCkdxHfV7S6jbW4xqbJrqHTA6HsuoWIwxlx+z0hZ1+0u5sO4UBqsDZ5iZsDGxWEbGtCsJ6mgNh8oof+8Icc+PR28xduq9ZC8h0WMlJiayYMECVq5cSWhoKNOnT/d2SEL0as7yBipXHUUzQdCU5uNSqqurWbZsGREREdx1111tTlZqtp+jen0+ODXQKXQWDX2QCUN0IPpQf/QWE7oAI7oAAzqLsen//fRNrQI6zdOaglJNLS4uhXK5UU6FsrtwNzipXHmY0tf3Ef3EKDSdRv3+89R9W4DL6kYzgf/wKCxj4ju8FcQyIpqAjKimzRKzS6j+6hzWDWdQyUGEZ8bgnx6BPtA766F42jIuM0DYWyRhEd3SkCFDmDFjBhs2bCA0NJTRo0d7OyQheiWlFOX/PABAzLNjMIT6eV5rbGxk2bJl6HQ67r33XszmtrUcuOocWDeexhDuT9CNSfgPjrhsd9P1in5yNKWv7uH8f2eDWwO9wi81lNCxffAbGIqm77xWXE2n4TcgjNgBYbhtThoOllG27zyVn+VR+VkeJAcTlhGN3+DwZtOTO1vF7hKc0f7ogn1nATlJWES3NWHCBKqqqli7di3BwcEMGjTI2yEJ0evYz1hxlNiJXDq0WbLicrlYuXIl1dXVPPjggwQFtX5gKjTtWVT+3kFQEPVwRqeuvKoPMhH9xGiqN+bj1z8MvyERVx3T0ll0ZgOW0bFYRsfiqnPQ8F0Z5TmlVH6Wh/YpOIONGGICCIwNxBhjwRgTgCEmoENjdVXbKF17Ck5UEbUw1afG1UjCIrotTdOYNWsW1dXVfPjhhyxdupT4+HhvhyVEr9J4rBLNpDAPDPMcc7vdrFmzhtOnT7NkyRKio6NbfT2lFI3HKqn8+AjuOjeRD6R3yTLx+mAT4XdefXpyV9JbjASOjSNwbByuOge2k1U0FNRQU1xL5YELGKu/X5PKGWDA5a9HXewWM1pM+AWZ8As0oQ80NnWVXfzQW4wtBtG6auw0nqjEeqQcx9FKlFFHxJ0DCciM6upiX5UkLKJb0+l03HnnnfzjH//g3Xff5b777iMxMdHbYQnRa7isjej8dWgXFyhzu92sXr2anJwc5s+fT79+/dp0vZptBVjXncYYbybq4XSM0bJ8gd5iJGB4FAHDo7i0I5Hb7sJZWo/jfD2uKhvuegeOWjv2Wgeuwlrq6h001DvROVuORnYbdTgDDKDX0NlcGOqcADhiAwib0ofgGxI6pevtevleREK0kclkYsmSJSxfvpx3332XRYsW0b9/f2+HJUSvYIwJpH5/KcrlxqXcfPLJJxw5coT58+eTkZFx7Qv82MXVVaOfGuNT3RG+RmfSY+oThKnPlbvalFIohxt3rQN3vQNXneP7/691oFxu9H4G9BH++A0M9doA39aShEX0CH5+ftx///2sXLmS5cuXc/fdd8u+Q0J0AVNSELg16vIr+HTXl+Tn57Nw4cJ2//7ZTlegD9VJstIBNE1DM+nRhesh3O/ab/BxsoCF6DFMJhOLFi0iNTWVDz74gNzcXG+HJESPZ0oMxhnsZtn7yzhz5gz33Xdfu5MVZ5UN2wkrgTckd2yQokeQhEX0KAaDgbvuuovMzExWrVrF7t27vR2SED1afWM96wK/o9xRw4KB00lJSWnXdZRSVK/LQzNoWMbEdHCUoieQLiHR4+h0OubOnYvZbOaLL77AZrMxefJkb4clRI9jtVp59913abA1sHDEbMy76qg0HCV03qA2LeeulMK6Pp+G3ApC7+iPziyPJtGS/FSIHkmn0zFz5kzMZjObN2+msbGR6dOnS7+4EB2koqKCd999F7fbzdKlS4mMjKQ2ppCq1Sdp+K6MkDkDCciM9sweuhJ3vYPyFd9hO1FD8PREAsfL0gTi8iRhET2WpmncdNNNmM1mNmzYgM1mY/bs2bL3kBDXqbS0lHfffReTycQDDzxAaGgoAIETEjD3D6Nq9TEqVx6natUxjHH+mFOjMcVa0IeY0Ux6XFU2nFWNNH5XSuOJajS9RsT9g/FPj/RuwYRPk4RF9HgTJ07EbDazZs0abDYbd9xxR7s2XxNCQGFhIe+99x7BwcEsWbKEwMDAZq8bowOIemgE9oIaGo5W0Hj0PDVfnQVny2vpQ3UE35KMZVQseh9aAl74JklYRK8watQozGYzn3zyCXa7nbvuugujsXN3IBWipzl9+jTLly8nOjqa++67D39//yuee2mNkJDpfVFuhbvOgavahnK40YeY0Qeb2jTORQj5aRG9Rnp6OosWLeLkyZMsX74cm83m7ZCE6DaOHz/Oe++9R0JCAkuWLLlqsvJjmq5ph2VTnyDM/UIwhPtJsiLaTH5iRK8yaNAg7r//fgoLC3nzzTcpLS31dkhC+DS32822bdtYvnw5/fv3b9euy0J0BElYRK+TnJzMI488gqZpvPnmmxw8eNDbIQnhk+rr61mxYgVbtmxh6tSpLFy4ULpShddoSqmWOyN1Q1arlZCQEKqrqwkODvZ2OKIbsNvtrFmzhoMHDzJ27FhmzJiBwSDDuoSApsG1H374ITabjQULFjBw4EBvhyR6qNY+v+VfZ9FrmUwmFixYQFJSEuvWraOwsJB77rmHkJAQb4cmhNcopdi7dy/r1q0jJiam2bRlIbxJWliEAAoKCvjwww+x2+3ceeedDBgwwNshCdHl7HY7a9euJTc3l9GjRzNr1ixpdRSdrrXPb0lYhLiovr6eTz75hLy8PG688UamTJkii8yJXqO8vJwPPviAiooK5s6dS0ZGhrdDEr2EdAkJ0UYBAQHce++9bNu2ja+//pqCggIWLFhAQECAt0MTolMdPnyYTz/9lKCgIB5++GFiYmTzQeF7pIVFiMvIy8vj448/xmQycc8995CQkODtkITocC6Xi02bNpGVlcWQIUOYN28efn5+3g5L9DLSJSTEdaqqquLDDz+kpKSEWbNmMXr0aNk8UfQYVquVjz76iIKCAm655RbGjx8vP9/CKyRhEaIDOJ1ONmzYwO7duxk2bBizZ89u0wqfQviivLw8Vq1ahU6n4+677yYpKcnbIYleTMawCNEBDAYDs2fPJjExkc8//5xTp04xc+ZMhg0bJn+Nim6npqaG9evXc+jQIVJSUliwYEGLzQuF8FXSwiJEK1mtVr788ksOHz5MSkoKc+bMISIiwtthCXFNbrebffv2sWnTJnQ6HTNmzCAjI0OSbuETpEtIiE5y/PhxvvjiC2pqapgyZQqTJk2StSqEzzp//jxr1qyhoKCAESNGcMstt8jMN+FTpEtIiE4yaNAgkpOT2bp1K1u3buXgwYPcdtttJCcnezs0ITzsdjtbt24lKyuL8PBwHnjgAfkZFd2atLAIcR1++NdrZmYmt9xyCxaLxdthiV7u+PHjrF27ltraWqZOncrEiROlFVD4LOkSEqKL/HB8gKZpzJw5U8YHCK+QcVaiO5KERYguVltby/r16zl48CDJycnMmTOHqKgob4clegG32012djabN2/GaDQya9Ys0tPTJWkW3YIkLEJ4SV5eHmvXrsVqtTJp0iQmT56M0Wj0dliihyouLmbNmjUUFRUxatQopk+fLmsFiW5FEhYhvMjhcLB9+3Z27NhBYGAgkydPZsSIETKOQHSYCxcusG3bNg4dOkRUVBS33XabLAAnuqXWPr+vayval19+GU3TeOaZZzzHPvnkE2bOnElkZCSappGTk9Oqa3388ccMGTIEs9nMkCFDWLVq1fWEJoRXGY1Gpk2bxuOPP05SUhJr167llVde4dtvv8XhcHg7PNGNnT9/ng8//JDXXnuNM2fOcOutt/Loo49KsiJ6vHb/uZednc0bb7zB8OHDmx2vq6tj0qRJ3H333Tz88MOtulZWVhYLFy7k97//PfPnz2fVqlXcc8897Nixg3HjxrU3RCG8LioqirvuuoupU6eyfft2vvzyS7Zv387EiRMZPXo0JpPJ2yGKbqK4uJht27Zx5MgRQkJCuO2228jMzJRWO9FrtKtLqLa2lpEjR/L666/z0ksvkZmZyf/8z/80O+f06dP069eP/fv3k5mZedXrLVy4EKvVyrp16zzHZs2aRVhYGCtWrGhVTNIlJLqD8vJytm/fzoEDB/Dz82PixImMGTMGs9ns7dCEjyosLGTbtm0cO3aMsLAwJk+eTEZGBnq93tuhCdEhOnXhuCeeeII5c+Ywffp0XnrppXYHeUlWVhb/9m//1uzYzJkzWyRBQnR3ERER3HHHHUydOpUdO3awZcsWvvnmGyZMmMDYsWPx8/PzdojCR5w7d46tW7eSl5dHREQE8+fPJz09XRIV0Wu1OWF5//332bdvH9nZ2R0WRElJCTExMc2OxcTEUFJScsX32Gw2bDab52ur1dph8QjR2cLCwpg7dy6TJ0/mm2++YevWrezcuZPx48czbtw4meXRi505c4atW7dy6tQpoqKiuPPOOxk6dCg63XUNORSi22tTwnLu3DmefvppNmzY0OF/Cf54vQCl1FXXEHj55Zd58cUXOzQGIbpaaGgoc+bM8SQuO3bsICsri3HjxjF+/HjZ86WXUEpx+vRptm7dyunTp4mJieHuu+9m8ODBkqgIcVGbEpa9e/dSWlrKqFGjPMdcLhfbtm3j1VdfxWaztau5MjY2tkVrSmlpaYtWlx967rnnePbZZz1fW61WEhMT23xvIXxBcHAwt956KzfccANZWVmej8GDB5OZmUlycrI8uHqg2tpaDh48SG5uLiUlJcTFxbFw4UJSU1OlvoX4kTYlLDfffDMHDx5sdmzp0qWkpaXx61//ut19qxMmTGDjxo3NxrFs2LCBiRMnXvE9ZrNZBiqKHicoKIgZM2YwadIk9u7dS05ODgcOHCA4OJiMjAwyMjKIjIz0dpjiOjidTo4fP05OTg55eXkApKamcvPNNzNgwABZnVaIK2hTwhIUFER6enqzYxaLhYiICM/xiooKzp49S1FREQDHjh0DmlpRYmNjAfjJT35CQkICL7/8MgBPP/00U6ZM4Q9/+AO33347n332GZs2bWLHjh3XVzohuimLxcKUKVOYPHkyBQUF5OTksHv3brZv306fPn3IzMxk6NChMtalm1BKUVRURE5ODocOHaKhoYH4+HjPEvrS9SfEtXX4BP7Vq1ezdOlSz9eLFi0C4IUXXuC3v/0tAGfPnm3W3Dlx4kTef/99fvOb3/D888/Tv39/PvjgA1mDRfR6mqaRmJhIYmIis2bN4tixY+Tm5rJ27VrWrVtHWloaGRkZ9O/fX2aP+CCr1cqBAwfIycmhrKyMoKAgRo4cSUZGBtHR0d4OT4huRZbmF6Ibqqmp4cCBA+Tm5lJaWkpgYCDDhg0jMzPzqmO/ROdzOBwcPXqUnJwcTp06hV6vJy0tjczMTFJSUmRsihA/InsJCdELKKUoLi4mNzeXgwcPUl9fT1xcHMOHD6d///5ERUXJmIguYLPZOHPmDEePHuW7777DZrORlJRERkYGQ4cOlfV1hLgKSViE6GWcTid5eXnk5ORw4sQJXC4XgYGB9OvXz/MRFhbm7TB7BKfTyblz58jPzyc/P5/CwkLcbjehoaEMHz6cjIwMIiIivB2mEN2CJCxC9GJ2u93zQD116hTFxcUopQgNDSUlJcWTwAQGBno71G7B7XZTXFzMqVOnyM/P5+zZszidTvz9/T3fy5SUFMLDw6VFS4g2koRFCOHR0NDAmTNnPA/cCxcuABAdHe154CYnJ0vXxUVKKS5cuOBJ+E6fPo3NZsNkMtG3b19PghIdHS1jUoS4TpKwCCGuqKamxtOdkZ+fT1VVFZqmER8fT79+/YiLiyMiIoKIiAiMRqO3w+1USilqa2spKyujrKyMs2fPkp+fT21tLXq9nsTERE9Sl5CQILOxhOhgkrAIIVqtoqKiWQJTV1fneS00NJSIiAgiIyM9HxEREQQFBXWr7g+Hw0FFRQVlZWWUl5d7EpTy8nLPvmSaphEXF+fpNktMTMRkMnk5ciF6NklYhBDtVldX53mo//DhXllZidvtBsBkMnmSlx8mM+Hh4V5rlXG73dTV1bWIu6ysjKqqKs95/v7+l03CwsLCMBg6fHkqIcRVtPb5Lb+ZQogWLBYLFouFpKSkZsddLheVlZXNWifKysrIy8ujoaHBc55Op8NsNmMymTzbaFz6/2t91uv12O12bDbbFT9f6TW73e6JQdM0wsPDiYiIYMiQIc2SK4vF0mXfSyFEx5CERQjRanq93tMi8WOXWmUqKytpbGy8bELR2NiI1WptcfxSq83lGI3GyyY3gYGBhIeHt0iMAgICiIyMlNYSIXoY+W0WQnSIK7XKXItSCqfT6UlgXC5Xs8REZuEIIUASFiGEl2mahtFoxGg0SleNEOKK5E8XIYQQQvg8SViEEEII4fMkYRFCCCGEz5OERQghhBA+TxIWIYQQQvg8SViEEEII4fMkYRFCCCGEz5OERQghhBA+TxIWIYQQQvg8SViEEEII4fMkYRFCCCGEz5OERQghhBA+TxIWIYQQQvi8HrNbs1IKAKvV6uVIhBBCCNFal57bl57jV9JjEpaamhoAEhMTvRyJEEIIIdqqpqaGkJCQK76uqWulNN2E2+2mqKiIoKAgNE3r0GtbrVYSExM5d+4cwcHBHXptXydll7JL2XsPKbuU3RtlV0pRU1NDfHw8Ot2VR6r0mBYWnU5Hnz59OvUewcHBve4H+RIpu5S9t5GyS9l7G2+W/WotK5fIoFshhBBC+DxJWIQQQgjh8yRhaQWz2cwLL7yA2Wz2dihdTsouZe9tpOxS9t6mu5S9xwy6FUIIIUTPJS0sQgghhPB5krAIIYQQwudJwiKEEEIInycJixBCCCF8Xq9PWL7++ms0TbvsR3Z2NgDl5eXMmjWL+Ph4zGYziYmJPPnkk9fct+jGG29scc1FixZ1RbFapTPLbrPZeOqpp4iMjMRisTBv3jwKCgq6olit0pqy5+bmsnjxYhITE/H392fw4MG88sor17x2T6j39pa9J9Q7wNNPP82oUaMwm81kZma26to9od6hfWXvKfV+9uxZ5s6di8ViITIykp/97GfY7farXrun1Ht7yt7l9a56OZvNpoqLi5t9PPTQQyo5OVm53W6llFIVFRXq9ddfV9nZ2er06dNq06ZNKjU1VS1evPiq1546dap6+OGHm127qqqqK4rVKp1Z9scee0wlJCSojRs3qn379qmbbrpJZWRkKKfT2RVFu6bWlP3tt99WTz31lPr666/VyZMn1T//+U/l7++v/vznP1/12j2h3ttb9p5Q70op9dRTT6lXX31VLVmyRGVkZLTq2j2h3pVqX9l7Qr07nU6Vnp6ubrrpJrVv3z61ceNGFR8fr5588smrXrsn1Ht7y97V9d7rE5Yfs9vtKjo6Wv3ud7+76nmvvPKK6tOnz1XPmTp1qnr66ac7MLrO1VFlr6qqUkajUb3//vueY4WFhUqn06kvv/yyw+LtSK0t+09/+lN10003XfWcnlrv1yp7T6z3F154oU0JS0+q99aWvafU+xdffKF0Op0qLCz0HFuxYoUym82qurr6itfqCfXenrJ7o957fZfQj61evZqysjIeeOCBK55TVFTEJ598wtSpU695vWXLlhEZGcnQoUP5xS9+4dlV2hd1VNn37t2Lw+FgxowZnmPx8fGkp6ezc+fOjgy5w7Sm7ADV1dWEh4df83o9rd7h2mXvyfXeWj2x3q+lp9R7VlYW6enpxMfHe47NnDkTm83G3r17r3q97l7v7Sm7N+q9x2x+2FHefvttZs6cSWJiYovXFi9ezGeffUZDQwNz587lrbfeuuq17rvvPvr160dsbCyHDh3iueeeIzc3l40bN3ZW+Nelo8peUlKCyWQiLCys2fGYmBhKSko6PO6OcLWyX5KVlcXKlStZu3btVa/Vk+r9ktaUvafWe2v1xHpvjZ5S7yUlJcTExDQ7LywsDJPJdNVy9IR6b0/ZvVLvndJu4wNeeOEFBVz1Izs7u9l7zp07p3Q6nfroo48ue83i4mJ15MgR9emnn6ohQ4aoxx9/vE0x7dmzRwFq79697S5Xa3i77MuWLVMmk6nF8enTp6tHH330+gp3DZ1RdqWUOnTokIqKilK///3v2xxTd653pVpf9p5Y723pEvqx7l7vrS17T6n3hx9+WM2YMaPFPYxGo1qxYkWrY+qO9d6esnuj3ntsC8uTTz55zZHaycnJzb7++9//TkREBPPmzbvs+bGxscTGxpKWlkZERASTJ0/m+eefJy4urlUxjRw5EqPRyIkTJxg5cmSr3tMe3i57bGwsdrudysrKZtl3aWkpEydObHuB2qAzyn748GGmTZvGww8/zG9+85s2x9Sd670tZe9p9X69unO9t0VPqffY2Fi+/fbbZscqKytxOBwtWh+upjvWe3vK7pV675Q0qBtyu92qX79+6uc//3mrzt+2bZsCVH5+fqvvcfDgQQWorVu3tjPKztHRZb80GOuDDz7wHCsqKvLJQXjXKvuhQ4dUdHS0+uUvf9nue3TXem9r2XtSvV9yPS0s3bXeL2nroNvuXu+XBp4WFRV5jr3//vvXHHT7Y92x3ttTdm/UuyQsF23atEkB6vDhwy1eW7t2rfrb3/6mDh48qPLz89XatWvV0KFD1aRJkzznFBQUqNTUVPXtt98qpZTKy8tTL774osrOzva8Jy0tTY0YMcJnpvpd0tFlV6ppulufPn3Upk2b1L59+9S0adN8aprjJVcr+6WukPvuu6/ZlMDS0lLPOT213ttTdqV6Rr0rpdSJEyfU/v371aOPPqoGDRqk9u/fr/bv369sNptSqufWu1JtL7tSPaPeL03tvfnmm9W+ffvUpk2bVJ8+fZpN7e2p9d6esivV9fUuCctFixcvVhMnTrzsa1u2bFETJkxQISEhys/PTw0cOFD9+te/VpWVlZ5z8vPzFaC++uorpZRSZ8+eVVOmTFHh4eHKZDKp/v37q5/97GeqvLy8C0rTNh1ddqWUamhoUE8++aQKDw9X/v7+6rbbblNnz57t5JK03dXKfqU+4r59+3rO6an13p6yK9Uz6l2ppqmqlyv/pVbFnlrvSrW97Er1nHo/c+aMmjNnjvL391fh4eHqySefVI2NjZ7Xe3K9t7XsSnV9vWtKKdU5nU1CCCGEEB1D1mERQgghhM+ThEUIIYQQPk8SFiGEEEL4PElYhBBCCOHzJGERQgghhM+ThEUIIYQQPk8SFiGEEEL4PElYhBBCCOHzJGERQgghhM+ThEUIIYQQPk8SFiGEEEL4PElYhBBCCOHz/n9ReUXi2hfzMwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter user-picked max district diameter, or 0 to accept 2.57163  2.5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working on county  0 neighbors\n",
      "I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#CHANGE- do not use clipLines to find outcroppings.  Instead, find boundary counties with low neighborcounts\n",
    "print(\"Now finalize the map topology before any squish operations.  Plot countyPop/aDP\")\n",
    "preSquishCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "maxD = 0.\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"distances\")\n",
    "    if c not in cutCountyList:\n",
    "        plotPoly(countyGeom[c],0.2)\n",
    "        plotCenter(r3(countyPop[c]/aDP), countyGeom[c], 7 )\n",
    "        for cc in range(c+1, nCounties):\n",
    "            dist = getLongDist(countyCP[c],countyCP[cc],xScale)\n",
    "            if dist > maxD and cc not in cutCountyList:\n",
    "                maxD = dist\n",
    "                #print(c,cc,maxD)\n",
    "print(\"the max LAT-scaled distance between counties is\",r5(maxD))\n",
    "plotPoly(MAP)\n",
    "plotPoly(MAP.centroid.buffer(0.5*maxD))\n",
    "plt.show()\n",
    "inputD = float( input(\"enter user-picked max district diameter, or 0 to accept \"+str(r5(maxD))+\" \") )\n",
    "if inputD > 0:\n",
    "    maxD = inputD\n",
    "neighborCountyList = [list() for c in range(nCounties)]\n",
    "for c in uncutCountyList:\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"neighbors\")\n",
    "    for cc in uncutCountyList:\n",
    "        if cc > c and cc not in cutCountyList:  \n",
    "            if countyGeom[c].intersects(countyGeom[cc]) :\n",
    "                neighborCountyList[c].append(cc)\n",
    "                neighborCountyList[cc].append(c)\n",
    "print(\"I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\")\n",
    "hasFewNeighborCs, has1neighborCs = list(), list()\n",
    "plotPoly(MAP,0.15)\n",
    "for c in uncutCountyList:\n",
    "    if len(neighborCountyList[c]) == 2:\n",
    "        hasFewNeighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.2,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.5)\n",
    "    if len(neighborCountyList[c]) < 2:\n",
    "        has1neighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.1,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d63e9f85-336f-4e38-8ed7-75035e9845f4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "continuing from above, develop corner county blocks from each low-pop corner county until it hits 0.125 of 781460\n",
      "checking if county 9 with pop 14255.0 and only 1 neighbor is less pop than 97682 vs districtPop 781460\n",
      "checking if county 0 with pop 228996.0 and 1 or 2 neighbors is less pop than 97682\n",
      "checking if county 4 with pop 152595.0 and 1 or 2 neighbors is less pop than 97682\n",
      "checking if county 9 with pop 14255.0 and 1 or 2 neighbors is less pop than 97682\n"
     ]
    },
    {
     "data": {
      "image/png": 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KuEQeYcEikbyaPIyOHi11DCLyA8khySipL0FuRS4WD1ns3C4IAsbFjnM+P1t5Fv+68i8IgoBwXTgyU7peXoVILliwSMRqt0Kj5BwzROQdk+InYVL8pE73GRY5DMMih+F4+XFcM17roWRE3sExLEREfYwAAcnByVLHIHILCxYJ+Mmd5ETUS5nt5jaDdYnkjgWLBApMXPCQiKQzKX4SLwlRr8OCRQJFdUVICk6SOgYR9WFDI4ZiV8EuGM1GqaMQuYQFiwREiJxGm4gkFR8Uj1nJs3DqxikUmgqljkPUJRYsRER9lCAImJY4DZdqL8Hq4Ky4JG+8rbmHNduaoVPqpI5BROQ0LWEadhfsRrguHOeqzuG7g76LEE3vWJON+g4WLD3sQvUFDA7nCs1EJB8apQbDo4YjRBOCcbHjsLNgJ8bFjkOYLkzqaEROvCTUw+osdTBoDVLHICJqJSEoAcGaYAiCgNtTbsfpytMc20KywoKFiIhaEQQB0xOno6q5Cmcrz0odhwgACxYiIurAqOhR0Kl02F24G43WRqnjUB/HMSw9qKqpCuG6cKljEBG5bEDoAKSEpCCnPAeNtkZEBURhaPhQTs1APY4FSw+6WHMRY2PGSh2DiMgtKoUK4+PGAwDKG8rxZfGXECBgRNQIBGoCoVaoJU5IfQELlh5kc9igVvJ/bCLqvWICYxATGAOH6MDh0sNwiA5MT5wudSzqAziGpQeJ4KKHROQ/TGYTpiVMkzoG9REsWHqIQ3RAAK/5EpF/KG8oh0qhwhf5X6DZ1ix1HOoDeEmoh1wzXkP/0P5SxyAi8oq4oDjEBcWh0dqIrVe2YlbyLEToI6SORX6MPSw9pLS+FPGB8VLHICLyqgB1AO4bfB8u117Gv6//G6LIS9/kGyxYehBvAyQifzUhbgLSwtKws2AnSupLpI5DfogFCxEReU2EPgL/uvIv5FbkSh2F/AzHsPSAJlsTdCqu0ExE/sXusONQ6SHnDQVhujCkhadhTMwYiZORP2LB0gMuVF9AWnia1DGIiLzq39f/jXGx46BX6aWOQn0ALwn1gHpLPYI1wVLHICLyKhEiixXqMSxYiIjII8GaYFyoviB1DOojWLAQEZFHRkSNQJ2lDlVNVVJHoT6ABYuPVTZVIlzPFZqJyD+NjRmL05WnpY5BfQALFh+7WHMRg8IGSR2DiMgnBEGARqGByWKSOgr5ORYsPmZ32Ln0OhH5tUnxk3Co5BDXFCKf6lbBkpWVBUEQsGLFCgCA1WrFypUrMXz4cAQGBiI+Ph5LlixBaWmpy21u3rwZgiDg7rvv7k40IiLqIYIgYHbKbOwq3AWz3Sx1HPJTHhcs2dnZ2LhxIzIyMpzbGhsbkZOTg7Vr1yInJwdbtmzBxYsXsWDBApfaLCgowC9/+UtMm+Yfy5U7RAen4yeiPkGpUGJu6lzsLNgJu8MudRzyQx4VLPX19Vi8eDE2bdqEsLAw53aDwYCdO3fivvvuw+DBgzFx4kS8/vrrOH78OAoLCztt0263Y/HixXjuuefQv79/rGqcb8pHSkiK1DGIiHqEUqFEZnIm9hXvkzoK+SGPCpZly5Zh3rx5yMzM7HJfo9EIQRAQGhra6X7PP/88oqKi8KMf/cilDGazGSaTqdVDbkrqSpAYlCh1DCKiHqNT6ZAQlMAFEMnr3J6af/PmzcjJyUF2dnaX+zY3N2PVqlV44IEHEBIS0uF+hw4dwltvvYXc3FyXc2RlZeG5555zeX+p8JIQEfU1aeFp2F+8HwlBCVJHIT/iVsFSVFSE5cuXY8eOHdDpOl/Mz2q1YuHChXA4HNiwYUOH+9XV1eHBBx/Epk2bEBkZ6XKW1atX46mnnnI+N5lMSEpKcvn9RETkXdeM11BUVwQAvBxOXieIoii6uvMnn3yCe+65B0ql0rnNbrdDEAQoFAqYzWYolUpYrVbcd999uHr1Kvbs2YOIiIgO28zNzcWoUaNatelwOAAACoUCeXl5GDBgQJfZTCYTDAYDjEZjp705PcVsNyO3IhcT4iZIHYWIyKvKG8qRV5OHIHUQNEoNqpurAbQUKSxUyF2ufn+71cMya9YsnD7dekbDpUuXIi0tDStXrmxVrFy6dAl79+7ttFgBgLS0tDZtrlmzBnV1dXjttdd6ba8JV2gmIn90o/EG8mryMD1xOuot9Wi2NyM9Ml3qWNQHuFWwBAcHIz299V/MwMBAREREID09HTabDffeey9ycnKwbds22O12lJWVAQDCw8Oh0WgAAEuWLEFCQgKysrKg0+natHlzgO63t/cmJrMJBq1B6hhERF5T2VSJc1XncGvSrQCAIE0QghDk88+1OqxotjWjydYEhaBApN714QPkP9wedNuZ4uJibN26FQAwcuTIVq/t3bsXM2bMAAAUFhZCoeAku0REvUVVUxVO3ziNmckzW21vtDbiYMlBBKgDWm3XKrUYFzvOo886VHIIIr4eraBSqKBX6aFWqJFXnYd7Bt7jUbvUu3W7YNm3b5/zz6mpqXBlSMw339Oed955p3uhiIjIa2qaa3Dyxknclnxbm9eK6oowKnoUogKiWm0/VHLIo8+qt9RDqVBiYtzENq8dLDmIuf3metQu9X7s5vCBmuYahGpDpY5BRNRttc21yCnPabdYAVp6XiL0nY9VdMfRsqMYHzu+zfab6xTpVJ3foUr+iwWLD+TV5GFw+GCpYxARdYvRbER2eXaHxQoAiBChELzzVWKymBCkDmq3vX9f/3e7vS7Ud7Bg8QGr3QqNUiN1DCIij4miiIMlB5GZnOnRBJjfHIPiqmNlxzA2dmyb7SaLCXqVHiqFV4ddUi/DgoWIiNowWUxICk7qsdm6jWYjgjXB7fauZJdlt3uZiPoWFixeJooip+Mnol6vuL64R6fWP1Z+DGNj2vau3Gi8gQhdBH+vEgsWbyuqK0JScO+c7I6I6Kaa5hqE68J77LNCtaHtFiWnbpzCyOiRPZKD5I0Fi5cVmAqQHJwsdQwiom7rqlfDard2OK5EgOs9IjkVORgdPbrN9iJTERKDueI9teAIJh9g1yUR9RZ1ljo4RIfzuSiKzpllu1LWUIbYwNhufX5lU2WHl3wu1lzErJRZ3Wqf/AcLFiKiPmz71e0YFjms1Ta1Qo2MqIwu31vWWObSfp05eeMkbktqe9v0xZqLuCXslm61Tf6FBYsXWe1WqJVqqWMQEbksMTjR48ULzXYztEptu6+5cltzRWMFovRR7fauFJmK2LtCrXAMixfl1eRhUNggqWMQEflcVVMVapprutXG6Run2+2hOXXjFFeApjbYw+JFNc01/J+MiHqtsoYyXKy5CJWgwuSEyW1eL28ohwgR56vOI1QXirv639VhW10Nuu1o/IsoirjReKPbl5rI/7CHhYioD6tursb5qvO4UnsFZyvPYnridEQFROFq7dVW+zVYG3Cm6gyMZiMmxE3AqOhR3brB4GzV2TZjZ4D/zMfSzmy3RCxYiIj6sFnJsxCuC4dCUDjHjAwMG4gCU4FzH4fowIHiA5iZNBODwwcjQB3Qrc8sqS9BXGBcm+02hw31lnoYtIZutU/+iZeEvOTmtNJERL1JgDqg3QLklrBbcLjkMBxoueV5SsIUtxY57GzQ7YWqC+0OqP3q+leYGM8FDql9LFi8JK86j9dcichvJAUndWvWbovdgoMlB9uMZWm2N7c7GZxDdMBsN0Ov0nv8meTfWLB4idluhk6lkzoGEZEs3Jbcdm6VzigEBVdjpk5xDAsREcmCO9P5U9/DgsULRLHrCZKIiKhzSoUSVodV6hgkUyxYvKC0obRHl2EnIvJHQ8KH4ELVBaljkEyxYPGCfGM+Ug2pUscgIurVwnRhqDXXSh2DZIoFixc4RIdbt/sRERGRe/gtS0REsiEIAuwOu9QxSIZYsHST1WGFUqGUOgYRkV8YGDoQl2svSx2DZIgFSzddrrnMFZqJiLwkJjAGFY0VUscgGWLB0k2VTZWI1EdKHYOIyG90Nq0/9V0sWIiISFZCNCGoba6VOgbJDAuWburO8upERNTW8MjhOFV5SuoYJDMsWLqh3lKPQHWg1DGIiPyKUqHkDOLUBguWbrhQfQGDwwZLHYOIyO9E6iNR2VQpdQySERYs3dBka0KAOkDqGEREfmdoxFCcqzondQySERYsREQkO4Ig8LIQtdKtgiUrKwuCIGDFihUAAKvVipUrV2L48OEIDAxEfHw8lixZgtLS0k7b2bRpE6ZNm4awsDCEhYUhMzMTR48e7U40n+P/SEREvhUbGIvr9deljkEy4XHBkp2djY0bNyIjI8O5rbGxETk5OVi7di1ycnKwZcsWXLx4EQsWLOi0rX379mHRokXYu3cvjhw5guTkZMyePRslJSWexvO58sZyxAbGSh2DiMhvDQobhIs1F6WOQTKh8uRN9fX1WLx4MTZt2oR169Y5txsMBuzcubPVvq+//jrGjx+PwsJCJCcnt9vee++91+r5pk2b8PHHH2P37t1YsmSJJxF97mrtVUyImyB1DCIivyUIAqeOICePeliWLVuGefPmITMzs8t9jUYjBEFAaGioy+03NjbCarUiPDy8w33MZjNMJlOrR0+yi3auIURE5GOJwYkoNBVKHYNkwO2CZfPmzcjJyUFWVlaX+zY3N2PVqlV44IEHEBIS4vJnrFq1CgkJCZ0WRFlZWTAYDM5HUlKSy+0TEVHv0N/QHwWmAqljkAy4VbAUFRVh+fLlePfdd6HT6Trd12q1YuHChXA4HNiwYYPLn/Hb3/4WH3zwAbZs2dLpZ6xevRpGo9H5KCoqcvkzusvusEMpsHeFiIiop7g1huX48eOoqKjAmDFjnNvsdjv279+PN954A2azGUqlElarFffddx+uXbuGPXv2uNy78uqrr+Kll17Crl27Wg3mbY9Wq4VWq3UnvtdcMV7BgNABknw2ERFRX+RWwTJr1iycPn261balS5ciLS0NK1eubFWsXLp0CXv37kVERIRLbb/yyitYt24dvvjiC4wdO9adWD2uorECg8IGSR2DiKjPEEWRA3D7OLcKluDgYKSnp7faFhgYiIiICKSnp8Nms+Hee+9FTk4Otm3bBrvdjrKyMgBAeHg4NBoNAGDJkiVISEhwjoP57W9/i7Vr1+L9999Hamqq8z1BQUEICgrq9kF6G+dgISLqOTen6Y8KiJI6CknIqzPdFhcXY+vWrSguLsbIkSMRFxfnfBw+fNi5X2FhIa5f/3oyoA0bNsBiseDee+9t9Z5XX33Vm/GIiKgXSglJQb4pX+oYJDGP5mH5pn379jn/nJqa6lLvwzffAwD5+fndjdFjGq2NXD+IiKgHBagD0GRrkjoGSYxrCbnpQvUFpIWnSR2DiIioT2HB4qZGWyMC1YFSxyAi6lNUChUsdovUMUhCLFjcxAG3REQ9b3jkcJypPCN1DJIQCxYiIpK9YE0w6q31UscgCbFgcUNlUyXC9R2vb0RERL5ld9iljkASYcHihks1lzAolBPGERFJYWT0SJy8cVLqGCQRFixusIt2qJVqqWMQEfVJIZoQ1FnqpI5BEmHBQkREvUZ0QDRK60uljkESYMHiIt4dREQkvSERQ3Ch+oLUMUgCLFhcVFxXjKTgJKljEBH1eXGBcSiqK5I6BvUwFiwuKqgrQHJwstQxiIj6vCERQ3C+6jx7vvsYFixu4NLmRETyMC52HLLLsqWOQT2IBYuLWMkTEclHmC4MFocF9RZOJtdXsGBxgdVhhVKhlDoGERF9w+T4yThUekjqGNRDWLC44HLNZQwMHSh1DCIi+gaFoMDAsIG4ZrwmdRTqASxYXFDZVImogCipYxAR0bf0N/RHgalA6hjUA1iwEBFRr6YQ+FXWF6ikDkBERNQdMQExKGsoQ2xgrNRRvMbqsCLfmI9acy0ECFAqlEgNSUWYLkzqaJJhweIC3s5MRCRfA8MG4kjpEb8oWC7XXEZRXRG0Si36h/ZHUnASRIiwO+zIN+XjTOUZWBwWpEekIyYwRuq4PYoFCxER9WoKQQERvX/qicOlhxEbEIuZyTPbfT09Mt3557OVZ3G++jxCtaEYETWiT/zDmgWLCwQIcIgOXiclIpKp3j5X1rmqc4gPjEeqIdWl/YdFDgPQclPIl8VfQoCAIRFDEB0Q7cOU0uI3sAviguJwveG61DGIiKgDvb2HpaKxwuVi5Zsi9ZGYkTQD0xOno7iuGAdLDsIhOrwfUAZYsLggKTgJhaZCqWMQEVEHYgNjUVpfKnUMjzhEB1SK7l3wEAQBo2NGY3T0aOwq2IXiumIvpZMPFiwuUCvUsIt2qWMQEVEHBoYOxKWaS1LH8IhCUMBsN3ulrQB1AGanzkZlUyXOV533SptywYKFiIh6PUEQIELstWNZhoQPwef5n6PJ1uTS/iX1Jfgo7yNkl2W3e8wjo0dCEATkVuR6Oal0BLG3nt1vMZlMMBgMMBqNCAkJ8Xr7h0q6Xq+iq2uoKoUKAaqAlof6658apcZbMYmI+qyqpioU1xdjRNQIqaN4xCE68EX+F7gj9Y4u7/opMhVBhIgQTQgOlx7G9MTpCNIEtdnvau1VVDVXYVzsOF/F7jZXv795l5CLpiRM6XYbVrsVjbZGNNma0GhtRHVzNRqtjbA6rO3uL0KEgI7/0ooQoVVqEagOdD4CVAHQq/R94hY3IqJvitBH4EzlGaljeEwhKDAhbgKOlR9zucAI1YVibr+52FmwE2NjxyJcF97q9f6h/aGuU+NExQmMih7li9g9hgVLD1Ir1TAoDTBoDV5pTxRFNNub0WhtRKO1EZVNlWiwNqDZ1tz+/t/oAbpZCH2zKBIhQq1QQ6/StxQ+aj3UCjVUClXLQ2j5qRSUXL2aiGRJpVDBardCrVRLHcUj4bpwnLKe6nQqjSZbE/JN+UgNSQXQcjlsdupsfH7tc8xKmQW1ovWxJwUnoaS+BDXNNb16plxeEqJWWvUC2RphtVthE22wO+ywOWywi3ZYHdY210y76g3qiMVu6XCSpHxjPorri9tt99uX3/QqPVJCUhChi/C4d8loNuJG4w2IEKEQFF8/oIBCoXAWbGqlGirh65/szSKSj0ZrI85WnZX1JZCuVDZVoqyhrNVEcTedrTqLi9UXMSVhCqL0Ua1+/1jsFhwqOdTu71RRFPFFwReYkzJHdr+zeEmIPOLtXqCuHCw52OFrSoUSIZoQZERldNlOo7URhXWFyKvOa7X9ZmETpgvDsIhhzu1muxnnq86jzlLn3BaiDUFMQIxzokAHHC0/RQesdisaHA2wOWywOqytfgItvwzc/SUgQGhTeHW07ZvH0tVlwgBVAEI0IQjWBCNYE8xLhNSnBKgDOuxl7i0i9ZE4eeMk0tG2YEkOTobFbml3gjiNUoNQXSiqmqoQoY9o9ZogCBgfOx5Hy45iQtwEn2X3JRYsJFsxATEuz38ToA5AWnhah6/vLtiNcG041Eo1mqxNuFBzARPiJiBE41+9caIoosnWBJPFBJPFhOL6YjTbmtsdEN5R4dNVb5kAARqlBmqlGhqFpuXPCrXz583taoWalw6JPDTAMADnq85jSMSQVtuPlx/vdEzlyKiR2F+8H7cm3drmtXBdOJSCEpVNlYjUR3o9s6+xYCFJdfTFmF2WjcOlh/HYiMe88jljY8eiqqkKteZaKAQFbk+53Svtyo0gCC13oKkDfLYQnEN0wGw3w+qwwmK3wOawwWK3oN5S37LNYYHV3vKzsyvOzfZm9DP0Q39Df5/kpL5LIShgd9h7dcGcakjF4dLDqG6udg6kLa4rRrguvM0YlW+62ZvaUa9vRlQGjpYdxdSEqb4J7kMsWEhSHd0KHqAOwLjYcV675dug7bnLXP5OISigV+mhh75b7VjtVuRU5MiiYGm0NqKisQI3mm7AbDd3ednt5qW7b+9X3liO7w78rq/jUhduCb0Fl2svY3D4YKmjdMvk+MnYW7gXKoUKDtGBQHUgxsaO7fJ9/Q39kW/KRz9Dvzav9ea5arpVsGRlZeFXv/oVli9fjvXr18NqtWLNmjXYvn07rl69CoPBgMzMTLz88suIj4/vtK2///3vWLt2La5cuYIBAwbgxRdfxD333NOdeNSLxQfGo6y+TOoY5ENqpdo5BshVoijC6rA6pwa4OTi8ydbU7kyhrgwEvznuJzogGhlRGdAqtW5l+qbOxmRRz4kJjMHFmou9vmAB0OFNCZ1JCknCgeID7RYsN/9+W+yWXjcHmMcFS3Z2NjZu3IiMjK8HRDY2NiInJwdr167FiBEjUFNTgxUrVmDBggU4duxYh20dOXIE999/P1544QXcc889+Mc//oH77rsPBw8exIQJvXNwELmmoy+U0oZSJAYn9nAa6mkl9SVuf8lrFJqWW+/VLXMOhevCoVfroVFoOLiYnPr634XOjn9i3ER8VfZVr7ss5FHBUl9fj8WLF2PTpk1Yt26dc7vBYMDOnTtb7fv6669j/PjxKCwsRHJycrvtrV+/HrfffjtWr14NAFi9ejW+/PJLrF+/Hh988EG77zGbzTCbv/4Xlclk8uRQSGYsdgsu117GNeM13NX/LqnjkI/dN/g+qSOQn7LarZ3OZeLvOutdVCvVsNqtHt3dKCWPzuSyZcswb948ZGZmdrmv0WiEIAgIDQ3tcJ8jR45g9uzZrbbNmTMHhw8f7vA9WVlZMBgMzkdSUpLL+Uk+LtdexoHiAzhQfAAHSw7iRMUJRAdEs1ghom4ZFzsOXxZ9KXUM2RoZPRK5N3KljuEWt3tYNm/ejJycHGRnZ3e5b3NzM1atWoUHHnig08lgysrKEBMT02pbTEwMyso6HsOwevVqPPXUU87nJpOJRUsvdP/g+6FT6aSOQUR+JkgThCERQ/Bl0Zft3uLb14XpwnrdwohuFSxFRUVYvnw5duzYAZ2u8y8Zq9WKhQsXwuFwYMOGDV22/e1uqa66qrRaLbRazwfHkTywWCEiX4kNjIVDdODUjVMuTUDZ1yQEJ6C4rrjXjBd065LQ8ePHUVFRgTFjxkClUkGlUuHLL7/EH/7wB6hUKtjtdgAtxcp9992Ha9euYefOnV1OlR8bG9umN6WioqJNrwsREZE74oPiUWuuRaO1UeoosjMobBCu1F6ROobL3CpYZs2ahdOnTyM3N9f5GDt2LBYvXozc3FwolUpnsXLp0iXs2rULERERXbY7adKkNoN1d+zYgcmTJ7t3NERERN8yNWEqDpUekjqGLAlCy1IkvYFbl4SCg4ORnt56bYPAwEBEREQgPT0dNpsN9957L3JycrBt2zbY7XZnz0l4eDg0mpZ7vpcsWYKEhARkZWUBAJYvX47p06fjN7/5Db7zne/gn//8J3bt2oWDBzmnARERdY9CUGBQ2CDkVef5xdwsruhoUs5vGxk9EjnlOS5NSCc1r97vVVxcjK1bt6K4uBgjR45EXFyc8/HNO34KCwtx/fp15/PJkydj8+bN+POf/4yMjAy88847+PDDDzkHCxEReUVKSAqK64uljiE7IZoQ1FvrpY7hEkHsrXP0foury1MTEfnKwZKDvW4yrr6kuK4YDdaGPtHLcqjkUKeLJH7TyRsnkRycjDBdmI9Ttc/V7+++OaMOERH1OYnBibjecL3rHfuYjMgMnK48LXWMLrFgISKiPkOr1KLJ1iR1DJ9zdQwL0DLwtjdcbGHBQkREfcaYmDHIKc+ROobPubLw5zelGlJx1XjVR2m8gwULERH1GRqlBlaHVeoYspMSkoLiumJZ3+LMgoWIiPqUuMA4lNSXSB1DdtQKNXYX7pY6RodYsBARUZ8yOHwwLtVckjqG7EyKnwStUr5L3rBgISKiPkdA75nhtSdplBpY7BapY7SLBQsREfU5I6JG4OSNk1LHkJ1hEcNwpvKM1DHaxYKFiIj6nFBdKIxmo9QxfMbTHqRgTbBsZ75lwUJERH2SWqH22zuG1ErPj83dW6J7CgsWIiLqkwaFDcLlmstSx/AJtUINq92zgkWn0slycj0WLERE1CdFBUShsqlS6hg+0Z0eliHhQ3C+6ryXE3UfCxYiIuqzBEGelz+6S61Qe3y3T5AmCA3WBi8n6j4WLERERH5Go/C/GX1VUgcgIvI3oiiirKEM14zXYBftiAqIQlp4mtSxqB1yHWDaXWql5z0sQEvPk0N0QCHIp1+DBQsRkZcYNAYcKD4AQRAQGxCLCXET0GhrxJXaK1JHow64s6pxb6JWqFFv8fz25H6Gfsg35qN/aH8vpuoeFixERF4yPGp4m2215lqEakN7Pgy5xF97WDQKDWwOm8fvjw+Mx6HSQ7IqWOTT10NE5IdMFhOCNcFSx6A+pjt3CQHyHIzMgoWIyIdiAmJQ3lgudQzqY/xxUjxeEiIi8qFIfaQs57Qg/6ZSqJwTxxnNRlyovgCL3QK1Uo2JcRNdauPm9P5yGXgrjxRERETkNQpBgesN13Gg+ADyTfkYFT0K0xKnwe6wu9xGYnAiiuuKfZjSPexhISIi8kPfG/S9NtvcuSsqKTgJh0sPIzkk2ZuxPMYeFiIiH/PXW2ep93HnriiFoIAoyufvLgsWIiIf89dbZ/0Bi8negwULEZEPNVoboVfppY5BHWAx2XuwYCEi8qEzlWeQHpkudQwij4TpwlDdXC11DAAsWIiIfEqn0sly5VsiV9wSegsu11yWOgYAFixERD41PHI4TleeljoGtcNkMSFIEyR1DFnTqXQw281SxwDAgoWIyKesDiscokPqGNSO0vpSJAQlSB1D9uQyTT8LFiIiHzpQcgDTE6dLHYPaUdlUiQhdhNQxyEUsWIiIfMQhOqBWqKFScI5OuZJL7wF1jQULEZGPKAQFb5uVKV6q6326VbBkZWVBEASsWLHCuW3Lli2YM2cOIiMjIQgCcnNzXWpr/fr1GDx4MPR6PZKSkvDzn/8czc3N3YlHRETUrsMlhzEhboLUMXpck60JdZY6t98nh+LO44IlOzsbGzduREZGRqvtDQ0NmDJlCl5++WWX23rvvfewatUqPPPMMzh//jzeeustfPjhh1i9erWn8YiIZCFUGyqbeSyoRZOtCSqFClqlVuooPW5W8ixkl2WjtL7U5fcMjxyOUzdO+TCVazy6sFpfX4/Fixdj06ZNWLduXavXHnroIQBAfn6+y+0dOXIEU6ZMwQMPPAAASE1NxaJFi3D06FFP4hERyUZicCJK60sRrguXOgr9h16ll82tuj1NEATclnwbssuyUWepw+DwwV2+x6A1yGIuIY96WJYtW4Z58+YhMzPTKyGmTp2K48ePOwuUq1evYvv27Zg3b16H7zGbzTCZTK0eRERyU1JfgrigOKlj0LfEBcah0FQodQzJjIsdh0ZbI/Kq86SO4jK3C5bNmzcjJycHWVlZXguxcOFCvPDCC5g6dSrUajUGDBiAmTNnYtWqVR2+JysrCwaDwflISkryWh4iIm+JCYjB2cqzUsegbxkSMQR5NXmw2q1SR5HMjcYbGBg20KV95bBIpFsFS1FREZYvX453330XOp3OayH27duHF198ERs2bEBOTg62bNmCbdu24YUXXujwPatXr4bRaHQ+ioqKvJaHiMhbogKiIAgCmm28iUBuZiTOwP6S/VLHkMT5qvNIC0+DQnCtDJDD3W5ujWE5fvw4KioqMGbMGOc2u92O/fv344033oDZbIZSqXQ7xNq1a/HQQw/hkUceAQAMHz4cDQ0N+PGPf4ynn34aCkXb/6BarRZabd8bMEVEvc/YmLE4Vn4MUxOmSh2FvkGtVCM1JBW5FbkYGT1S6jg9pqyhDKdunML9afe7/J5e18Mya9YsnD59Grm5uc7H2LFjsXjxYuTm5npUrABAY2Njm6JEqVRCFEWIovT/kYiIukOn0qHAVCB1DGrHgNABCNOF4UDxAamj9IjcilyU1Je4VazIhVs9LMHBwUhPb71MemBgICIiIpzbq6urUVhYiNLSllum8vJaBvTExsYiNjYWALBkyRIkJCQ4x8HMnz8fv//97zFq1ChMmDABly9fxtq1a7FgwQKPiyAiIjmZGDcRewv34takW13uhqeekRKSgiB1EJ4++DSemfQMNEqN1JG8zmg24mjZUQwJH4LE4ES339/rLgm5YuvWrVi6dKnz+cKFCwEAzzzzDJ599lkAQGFhYaselTVr1kAQBKxZswYlJSWIiorC/Pnz8eKLL3o7HhGRJAaEDkBsYCw+v/Y5JsdPRqguVOpI9A0R+gjolDqUN5YjKdg/buKw2q04WHIQABCoDkRmcqbHSxHI4ZKQIPrJNReTyQSDwQCj0YiQkBCp4xARtUsURRwqPYRIfSTSwtOkjkPfYLFb8EX+F7ij3x1QK9RSx/FYcV0xTt04Ba1Ki6kJU6FWqLvdq3ew5KDPxmC5+v3Nfkkioh4kCILzS2Rf0T6O05MRjVKDOalz8Nbpt1BvqZc6jtvqLfXYXbgb1c3VuLP/nZiVPAtapdZvLkFyCVEiIgkMCB2AmIAYfHbtM0xJmAKD1iB1JEJL0fLo8EdxqPQQFIICE+MmynK1bYvdgmNlx2ATbVAIChjNRlxvuI6lw5ZCqfD+2E+tUguz3SzpcgbyOwtERH1EkCYIc/vNxeHSw4jUR7o0TTr5nlKhxPTE6Wi2NeNQySGoFWpMiJvgk0LAHeUN5ThXdQ5muxnN9mbM7Te3xwqIIHUQ6ix10OqlK1g4hoWISAZO3TiFMG0YkkL8Y8CnP2m0NuJo2VEUmArwQNoDUCt7bnxLgakAuRW5CNYEI0IfgRFRI3rss7+pqK4IdocdqYZUr7ft6vc3e1iIiGQgIyoDuwt2s2CRoQB1AGYkzcCpG6fwZfGXCNGEYHTMaJ9eKrp5G3JycDK+c8t3fPY5rtIqtai11UqagQULEZFMDAwbiLzqPF4akqmMqAwAQIO1AV9d/wp20Q6FoECQOgj9DP28Ng7pTOUZ1FnqunUbsrcpBSXsDrukGViwEBHJRHJIMnYX7mbBInOB6kBMSZjifG6ymFBgLIDRYmx3f4WgQIgmBCGaEETqIxGgDuiw7dyKXOhVekyKn+T13N2hUqhgc9ikzSDppxMRUSuDwwazl6WXCdGEYHjU8A5ftzvsqLfWw2g24kzlGdRb6zEjaUab241P3TiFEG0I+hv6+zqy29QKNawOaVe29o+bs4mI/ERicCKK64uljkFepFQoYdAakBySjPFx4zExbiI+u/YZrPbWBUBxXbEsixWgZQxLs13aFcdZsBARycwtobfgUs0lqWOQjwSoAzA7dTb2Fe8DADhEB77I/8KjNX56Sr4pX+oILFiIiOQmJSQFRXVFUscgH7o5Xb7NYcP+4v2YljDNOahXjsoayjA5frKkGViwEBHJUKohFVeNV6WOQT40LWEaduTvQHJwcqcDcakFCxYiIhnqb+iPfGO+1DHIhzRKDe7sfyf6h8pz3IrcsGAhIpKp5OBkFJgKpI5BJAssWIiIZOqWsFtwpfaK1DGIZIEFCxGRjCUEJXAALhFYsBARydrg8MG8xZkILFiIiGQvLjAOpfWlUscgkhQLFiIimRsSMQQXqi9IHYP6MBGi1BFYsBAR9QbRAdEoruOU/dTzHKIDAqRfNZoFCxFRL5AemY6q5irsL96PnPIcqeOQH7hQfQE55TlwiI5O96torEB0QHQPpeoYCxYiol5iRNQITE+cDrtox8GSgzCajVJHol6kzlKHbVe3YX/xfuwv3g+loMSA0AH4/Nrnnf5dulp7FckhyT2YtH0qqQMQEZF7xsWOg81hw5nKMzBZTACAgaEDERcUJ3EykoMmWxMu11xGrbkWIkQYzUaEaEIQoA7A3NS5UCqUrfaf228uvsj/AnNS50AQ2l76sTqs0Cq1PRW/QyxYiIh6IZVChZHRIwEAoihid+FuBKgDYNAapA1Gkjl54yRqmmugV+mREpKC4VHDAQDZZdkYFzuuw/cJgoCJcROx/dp23JF6R6uCpqqpChH6CJ9ndwULFiKiXk4QBEToI6BS8Fd6X3Oj8QbOVZ1Ds70ZkfpIzEia0WafzoqVm0J1obgt+TbsLNiJO/rd4dx+ruqc5Ks038S/3UREfsBit0Cj0Egdg3ygvKEcV4xXYHfYIQgCrjdcR7Q+GgpBgXB9OKYnTm/3Uo679Cp9m1WjRYhtLiFJhQULEZEfsDls7GHxQ6X1pThefhx39LsDaoXap59ld9ihEL6+F0cURVncznwT/3YTEfkBAYJX/pVN8mA0G5Fdlo24oDjMHzC/Rz7zYs1FDAob5Hxe1Syf8SsACxYiIr8gh5lIyTtyynPQbG/GrORZPVqE3mi6gSERQ5zPC0wFGBI+pJN39CwWLERERDJQ3lCO05WnMSB0APoZ+kkdB022pjZjWqTEgoWIyA8025qljkBuMJqNCFIHOQe0Hi49jABVADJTMiXJI4ry76HjTLdERL2c1WGV1b+EqXPVzdX45PInKG1oWYH7bOVZ9Df0d86rI4VrpmttenXkVsSwYCEi6uUqGytlsdYLdczmsKGorghfXf8KJypOYFHaIpTWtxQsNeYaxAbGSpqvuK4YiUGJzuc2h63VHUNy0K00WVlZEAQBK1ascG7bsmUL5syZg8jISAiCgNzcXJfaqq2txbJlyxAXFwedTochQ4Zg+/bt3YlHRNQnNNmaoFfppY7R59kcNhSZinC49DAOFB9wPg6WHER2WTbsDjtGR4/GrORZ0Cg1sNgtEEURZptZ6ugA0GqA75nKM0iPTJcwTVsej2HJzs7Gxo0bkZGR0Wp7Q0MDpkyZgu9///t49NFHXWrLYrHg9ttvR3R0ND7++GMkJiaiqKgIwcHBnsYjIuozQnWhKGsoQ3xQvNRR/JooijBZTDhbdRZWuxWjY0YjSB2EnIoc1FnqoFFoEB8Uj3Gx41yaM0WEiM+ufYYpCVN6IL17TBaT7JZ58Khgqa+vx+LFi7Fp0yasW7eu1WsPPfQQACA/P9/l9t5++21UV1fj8OHDUKtbTnJKSkqn7zGbzTCbv65KTSaTy59HRORPQrWhOFN5RuoYfm3b1W0waAwI1gRjXOw4lDWUIac8B1aHFaOiR3k0X8n0xOk+SOq+0vpSxAfKv9j16JLQsmXLMG/ePGRmemc089atWzFp0iQsW7YMMTExSE9Px0svvQS73d7he7KysmAwGJyPpKQkr2QhIuptFIICDtEBh+iQOopf2pC7AUHqIExLnIaR0SOhVqgRrA5GTGAMMlMyZTW5mieu1F5B/9D+zuflDeWI0kdJmKh9bhcsmzdvRk5ODrKysrwW4urVq/j4449ht9uxfft2rFmzBr/73e/w4osvdvie1atXw2g0Oh9FRUVey0NE1NuMih6F3IpcqWP4pbTwNExLmNZqW6guFGnhaRIl8i4RYqsBthdrLsry2Ny6JFRUVITly5djx44d0Ol0XgvhcDgQHR2NjRs3QqlUYsyYMSgtLcUrr7yCX//61+2+R6vVQqvVei0DEVFvZtAa0GhrlDqG36lqqoJOqZPNAoA9QYQoy2Ue3CpYjh8/joqKCowZM8a5zW63Y//+/XjjjTdgNpuhVLp/UuPi4qBWq1u9d8iQISgrK4PFYoFGwxVIiYio5+XV5GFc3DipY/hMVVMVInRfX9KyOqyyXUTTrVSzZs3C6dOnW21bunQp0tLSsHLlSo+KFQCYMmUK3n//fTgcDigULd1SFy9eRFxcHIsVIiKSTLOt2eerJEsprzoP4+PGO5/L8Xbmm9wqWIKDg5Ge3vpAAgMDERER4dxeXV2NwsJClJa2TIiTl5cHAIiNjUVsbMvEOEuWLEFCQoJzHMxjjz2G119/HcuXL8cTTzyBS5cu4aWXXsKTTz7ZvaMjIiLyUL4xH6mGVKlj+JRdtLfqUamz1CFEEyJhoo55fRq7rVu3YtSoUZg3bx4AYOHChRg1ahT++Mc/OvcpLCzE9evXnc+TkpKwY8cOZGdnIyMjA08++SSWL1+OVatWeTseERGRSy7VXkJ/Q/+ud6QeIYhyWyzAQyaTCQaDAUajESEh8qwOiYh86WDJQUxNmCp1DL9w6sYpRAdESz5lvi/VWepwufYyRkWPAgCUNZTBaDZicPjgHs3h6ve3vBYKICKivufoJuD9+4GGSqmTAGhZSdlkMfl1sQIA56rOYWjEUOfzSzWXMChskISJOseChYiIpHP2E+DzVcDlXcBbswFjsaRxTBYTDpUcwpR4+U2X720WuwVa5dfTg8j1duabWLAQEfkJhaCA3dHxDOGyIorA/leBv/0AGPod4Gf/BuxW4M93ArWFPvvYq7VXcazsGCx2y39iiCiqK8KhkkPYV7QPZyvPYm6/ubL+4vYFq90q+7uh5HmzNRERuS1EE4I6Sx1CdaFSR+mczQxsfRI4tRmYsRq4dSUgCMDST4F37gL+PA94+F9AWGq3P0oUReRU5KDR2ggRIqL0URgaMRSfXfsMepUeOpUOScFJmBw/uU8VKWa7GWrl1wXKqcpTGB45XMJEXWPBQkTkJ0K1oagx18i7YGmoAj5cDJTkAN97Cxh+79evhSYDS7e3FC3v3AX8YCsQ7vpdOpVNlThfdR4iRJjtZuhVejRYGzA2Zmyb9X7u6n8XBEFoNSV9X6JRaFDVVAWH6IBCUKDeUo8gTZDUsTrFgoWIyE+EakNxufay1DE6diMPeP8+wNIAPLwNSBrfdh9D4reKln8BEQM6bfaq8SoKTYUI1YZicvxkKARFl70lfWmq/fYIgoCZSTPxryv/QqA6EIPDevbOIE+wYCEi8hOB6kA0WBukjtG+K3uAjx4GDAnAkq1AWErH+4bEAw9/CvxlPvDOPOAH24DIW9rd9VLNJTTbmjEjaYZPYvuzAHUA7uh3R6uBt3LWN/vCiIj8kCAIECHDqbWy3wLevbelR+WHX3RerNwUEtdStGhDgHfuBG5cbLOLKIoorCvE8Ch5j72Qs95SrAAsWIiIyFfsNuCzlcCnTwHjHgEWbQZ0bkzsGRzTUrQERLRcSvrWPKcnb5zEyKiR3s1MssWChYiIvK/ZCHxwf8ukcPN+B9z5W0DpwSiEoCgg8zmg5hqw89fA23OBgiMAgFpzbZvBtOS/OIaFiMiPCJDBrbnV11pmrq0vAx78OzBgZvfaS5nccovzkf8BRDuw+3mUfH8TNEpN+/ubSltunQ6OBdT67n02yQYLFiIi8p78Q8CHDwL6UOCR3UDkwO63qQ0CHj8OOGxA0b+x5+OFSDj5PiZP/a82u4qnP8aBz56EQvzPBHrqQCj0YYgMiERUYDxCgxMghMQD/aYDsendz0Y9hgULERF5x4l3gX+tAJInAvf9HxAQ7r22laqWR/8ZGDvgTuTlvANM/gXwjduTHcf+jB17f4VJA+bCMPIhoL4cqLsOm+k6quuKUWoqwPnyHNibqiHufw6hhhSkD70fiozvt8wBQ7LGgoWIyI9IcpeQww7sehY4/Adg9A9axqwofTfNe8jEx9Hw7vaW9YcGzQFEEbZDr+GLI7/BzCH3I+DO3wOKr4doqgBE/+cBALBZgCt7UH3qPRw8+hrs/34FQ+InIfbBf/gsM3UfB90SEZHnzPUtl4COvAHMyQLmv+bTYgUAED8K0aH9UHbsT4DDgabP/gtfHPkNbh/1EwTM++9WxUq7VBpg8B0I//5fMf2xE3AA0Fzd59vM1G0sWIiI/Igo9mAPS20R8PYc4NqBlluWJ/2sZU0gXxMEDBn1Q1woPoTKd+/Gl2ffxdzpz0GT+az7nx8YgVvHLMOZgOCWO5tItliwEBH5EbVSDbPd7PsPKjoKbLoNMJuAH+1ouTTTg4SM+xCgUOFa1TncseDPUEz4scdtqcb9CA6HFTjzdy8mJG8TxB4tx33HZDLBYDDAaDQiJMSNiYmIiPxIs60ZB0oOQK/6+nbeb9/qLEKE3WFHgDoAFrvFuT1QHYiR0SO7/pBTHwH/fByIHwXc/27LXClSKD8HBEUDgZHdbqrgL3dChIjUH3zmhWDkDle/vznolojIj+hUOtyecnuX+52tOot6Sz2mJExxbvv39X/DZDEhRNPBl4bDAex9ETjwKjDiAWD+ekAl4dTuMUO91lRK2t3Y/+WzSLU0ApoAr7VL3sNLQkREfdCwiGGYEDeh1bYJsRNw9PrR9t9gaQD+tgQ48LuWmWfv3iBtseJtKZMgiHag9ITUSagD7GEhIiIALYsnJgUnodBUiOSQb8xLYiwBPlgIVF0BFr4PpN0pXUhfiRoCQakBrucCgZGwn/k7Lhfsww1TEWA2QaEJxuhB34Fu2i9aLkNRj+MYFiIiamVXwS5kpmS2PCk+DmxeBCg1LXcC+fHssEf/OA6KujI0OCwQ1AEYHD8e0VHDIQRFwVqTj6PnNkN02DFlVhaEUYuljus3OIaFiIg8MiJqBHIrcjGy/DLwz2VAbAaw8D2/71kYMeJhNJYcQ1j694EBtwFqnfM1NYApt/4X6j5fie27fonbK/OgyXyuZ27jJgAsWIiI6FuidBE4uffXcGS/D0XG/cD8P7T68vZX2knL0OmoHH0Ygu/+X8w5NBSfH/kN5hmSIYx/pKfi9XkcdEtERF+zNKL4owegPPsPCLetBe753z5RrLhMEKCaugIzBt2DQwfWtUyeRz2CBQsREbUwleLcO7ejrOgQZs7fBGH6L3nJowNBc7IQpNKjfPtTUkfpM1iwEBERUJKDr96ZBVVTDcY+uA0YMl/qRPKmM2Dk1JXIKzoA1JVJnaZPYMFCRNTHiWe2YNfmu5Gij8agH+4B4kZIHal3GPodQKGEeEb+qzwXVjWi3NQsdYxu4aBbIqK+ShRh3vcydh37A2akzkbg3X8E1Pqu30ct9GHoFzsG+Ze2od+kx6RO064mix3rtp/Ah2f3AKIS05JHYNPi26BS9r7+it6XmIiIus/ahKqPl2Bf9mu4Y9wKBN77jiyLlQ0bNqBfv37Q6XQYM2YMDhw4IHWkVhKiM3C9Tr4Db986eAV/z/sM/2/qPXg2814cLD6GTQcuSx3LIyxYiIj6mroyXHtnNs4X7MOceX+EcsZKWQ6u/fDDD7FixQo8/fTTOHHiBKZNm4a5c+eisLBQ6mhOioj+cDTcAOxWqaO0a8elXEyInYCf3DoQD05MweLhd+C1I//ClRv1UkdzGwsWIqK+pLYQJ/98G+pqrmLqvZuBYXdLnahDv//97/GjH/0IjzzyCIYMGYL169cjKSkJb775ptTRvhaWCkF0AEZ59rJcrSlBRlyK8/nKOcMRoYvEyk/2SpjKM90qWLKysiAIAlasWOHctmXLFsyZMweRkZEQBAG5ublutbl582YIgoC77767O9GIiKg9DZUwO2wwijac+NtCoKZA6kTtslgsOH78OGbPnt1q++zZs3H48GGJUrUjrF/Lz5p8SWN0ZExcOj6/dMz5XK9R4tez5yC3/Dyy86slTOY+jwuW7OxsbNy4ERkZGa22NzQ0YMqUKXj55ZfdbrOgoAC//OUvMW3aNE9jERFRZxJGY/zyPEwb9wTqRAegD5U6UbsqKytht9sRExPTantMTAzKymR0G3HQf/I1VEqbowMPjB2OazUlOFFY49x2+5AYJIXE43d7/i1hMvd5VLDU19dj8eLF2LRpE8LCwlq99tBDD+HXv/41MjMz3WrTbrdj8eLFeO6559C/f39PYhERkSsEAcXXjyMxZjigM0idplPCt8bWiKLYZpukbmaR6TrCt6VF45aw/njkw49RVN0IAFAoBKyYPhPHSs/gdLFR4oSu86hgWbZsGebNm+d2UdKZ559/HlFRUfjRj37k0v5msxkmk6nVg4iIXFNYexUpMaOljtGhyMhIKJXKNr0pFRUVbXpdpCWj4qkdaqUCf10yHzqVFj96fxvE/xRWd2XEIzYoGr/fky1xQte5XbBs3rwZOTk5yMrK8lqIQ4cO4a233sKmTZtcfk9WVhYMBoPzkZSU5LU8RET+zmG3QqHqdKk/SWk0GowZMwY7d+5stX3nzp2YPHmyRKk600EPi6URsDR8/byhCsj7DDi6Cdj/KnDiXcB03afJooK1eGHuHFypLcLF8pa7g5QKAU9MuQ0HCnNwsbzOp5/vLW5NHFdUVITly5djx44d0Om8sxhWXV0dHnzwQWzatAmRkZEuv2/16tV46qmv13AwmUwsWoiIXBRrSEHZjTOIkzpIJ5566ik89NBDGDt2LCZNmoSNGzeisLAQP/3pT6WO9rWOLgkVZaPyyHqcK9wPhd0CUROI0MA41NRcgQARgqAC1Ho4LPUQAfSPHYOkua8C0UN8EnNi/whAFHC21IjBscEAgO+OTsTv9ofgzf1n8N/fn+STz/UmtwqW48ePo6KiAmPGjHFus9vt2L9/P9544w2YzWYolUq3Aly5cgX5+fmYP//rdSscDkdLOJUKeXl5GDBgQJv3abVaaLXy/dcBEZGcpcSNRfb5DxHncAAKec5wcf/996OqqgrPP/88rl+/jvT0dGzfvh0pKSldv7nHCP/pW/m6YHFkv4W9+36NqKA4TJvwFCymCqhO/hWXmvIw1G6H4ZY7oJ78JJA8EWiqAfK248KR9Tjw5xmY9r33gFu8N9ziJpVCgW9fvlIrFViUMQN/Ov4ZGszjEKiV9+T3bqWbNWsWTp8+3Wrb0qVLkZaWhpUrV7pdrABAWlpamzbXrFmDuro6vPbaa+w1ISLyAfXgO2E7sREoOQYkjZc6Tod+9rOf4Wc/+5nUMTpmNrWUAdqWXgvL3hex89gbmDl0ITDou3h/289xVVUJMUiLcFscpgSF4FLlSVg23w1BHwalOhBKQQm7tR6VSiWMJ/4Kgw8KltLaJkBwIESnbrX9+2OT8T9Hbdh++jq+P1be37duFSzBwcFIT09vtS0wMBARERHO7dXV1SgsLERpaSkAIC8vDwAQGxuL2NhYAMCSJUuQkJCArKws6HS6Nm2GhoYCQJvtRETkJUnjoddHwHTsLYTIuGCRPVNpS99KcDzE3A+w69gbmDN+BexqAz7c+gDKxXBMH/As7AMy8dt9/8YblUV4855FmKm7BFzZA9HaDJvDAoVKD2XCaGDQHJ/E/PzMdWiUAibfEtFqe2JYAEbHDsFfj3/lXwWLK7Zu3YqlS5c6ny9cuBAA8Mwzz+DZZ58FABQWFkIh0y5IIqI+QaHEmFGPYv9Xv8MMYzFgSHS/DVEELu8GogYBocnez9gb1P3nLqaLn2Hf8TcxLfV2NFTm4dDVz3DWnoF+49fj1ttaxqVMHnQPfvL+Pjz2yXv43+8twa2Z0yAAUHfcutdsvZCNCQnDEaBp+7X/4NjR+Pm/3sPVG/XoHxXUA2k8I4iiTG8ed5PJZILBYIDRaERISIjUcYiI5K/ZiNz/nYjYsAGIfehf7q0nVJSN61+sxN8aLuPJkU8At/4/3+WUs/xDOPLhvbDCgfiAWOh1Ybhsugp70n/h0TwLLj79cygVX/93tdgc+NG7e5B9/ST+dO8PMHWg6zebeKqgqgG3vbkBr9y5CN8d3bYwbbbaMe73/4MHMmZi9dzhPs/zba5+f7Obg4ior9IZMPLO15FbfhyOIxtce0+zCdV/ewi7P5iPmopTyGi2ACMf8G1OOUudgklPXcX0H+xGaEAkrpnycet9H8M08B4AIqoazK1216gU+NODMzE6Nh2Pfvx/OHzF9zPkvr7vNAzaINw5vP17wnRqJRYMnoq/n9sPm93h8zyeYsFCRNSXDczE9PQl+OzQOtgPvdb5jK3l53DhrVtxoeggZs1Zj+gBtyMmahhgSOi5vHKk1sNRcBjHqs9h6l1vAknjMXNwNDQqAR9lt11ZWqtS4u0HM5ERPQSPfPQecr4xbb6nNmzYgH79+kGn02HMmDE4cOAAAOC6sQn/yjuIR8dlQqfu+MaYReMGoKapCQcuyXOJAYAFCxFRnxdwx8uYNfIR7Dj8Mire/x5QcBiwWVpetFmAomzU//Nn2P3uHVApNJj8g13AiIUoMBYgJSJN2vByYG3C0SOvYNrABcDguQAAQ4AacwZMwv+d2AtrO70WOrUS7zw0GwPD++EH77+P89c9n639ww8/xIoVK/D000/jxIkTmDZtGubOnYvCwkK8+eUFaJQaPDSpX6dtDIsPQaohAf+XfcLjHL7GMSxERNTi3Fac3P00jI0VEAQlBHUAYKmHAyL0+kiMGr4YqikrAE0AAODIH8djXNJ0qOa9Km1uqeV+gH1f/BwzHjkERHw9b9i5UhMWvP1HZN2xqMM7cEzNVtz7p09Q2ViLLT98AKmRgW5//IQJEzB69Gi8+eabzm1DhgzB7Dvvwo7IOPxozB345eyhXbbz50PX8PKXW3D4yccREdRz85xxDAsREbln6AKM+NkJTH/wM0yb+Tymjvkppt7+W0y/fwvGPX4Kqpm/chYrAKBRKGGxNUkYWB4KTv4FyTEjWhUrADA0PgRTkkbhtYO7OxwbEqJT4/2HFyBIE4CFf/kbrtyohzv9CBaLBcePH8fs2bNbbZ89ezb++cUeKAUlfjTlFpfauntkAgQI2HKiyOXP70nyntaOiIh6llIFJIxueXQhISQJpbXX4NrXoZ9qrEZB+UlMn9X++nq/nDUe97zzv9h26jruHtX+WJ/IIC02P/xdfPetDzH7jxsRoFGgX1g8xsalYURSBIbGhSDWoEOQVtVmperKykrY7fY2C0IGGiJQWl6ErNG3IyxQ49KhhAVqcGvyOLyXux+PTB0gr1WxwYKFiIg8FBU2EFcu/6tvFyzX9gOio8Pp9IcnGjAxcQRe2rMdUwcuQWQHl1riQ/X44rHFOJpfjYvldcgpycf2K4fwl9yve7DUSgUMejVCdcGICohAXFAU9JZmAMDF8jqMMNsQqFXheEE1Psw5C4WgwKPTB7p1OIvHp2HpR1/h6LVqTOgf0fUbehALFiIi8ogybgTsuX8C6iuAoGip40ii4fIXCAhJ7HTivVfumY55//tX/PDdz/G3R+6CVtX+3TqGADVuHxqD24fGALgFQCZqGizIK69DRZ0ZlXVm3KhrRll9LcobKnGy4iyq64yAQoFVH23GC2fPANZwCJoKWOtNGD7gljZT8Xdl2sBIRAUGY0vuVRYsRETkJ/rdChECkLcdGPOw1Gl6nt2KUwVfYvQt8zrdLc6gx58Wfh8PvPcW/mvLV1j//UkuX24JC9S0rLTciXEfv48BGiUyb70HH58+ih9PXIw17/0Fmd/5jsuHcpMgCJg3aDL+ce5LvOQY22rSO6lx0C0REXkmOAaDE6fg7NH/6Xz+Fj9lO7QejeZaaEcv6XLf0clheGnO/fj08l5s3H/Zqzl++YtfYMsH/wfFpX3475kjcOCvv0NhYSF++tOfetTeXcMTYTSbkZ1f7dWc3cWChYiIPBY7eQUq6oqAK3ukjtKzSnPx5VfrMXXED4FY16az/+7oJPxw1Hy8euhj7LlQ7rUo999/P9avX4/nn38eI0eOxP79+7F9+3akpKR41N7IpFBE6AOw9VS+1zJ6A+dhISIiz4ki8jZOQZA+HAlLtkmdpmeY63HuT9OgUweg/w/3AirX7sIBAIdDxNK/7sHx62exZelSDIoJ9mFQzz2zNRfbLh7C8V8u8/lncR4WIiLyPUHA4MlP4XzZMSD/kNRpfO/KXlx4awbqmqrQ/7t/catYAQCFQsD/LLwVMQEx+MF7W1DdYPFR0O4ZFh+B2iYL7A759GmwYCEiou4Z9l0MCBuEC9ufBMx10mYx1wNlp4GaAuBGHlCaCxR+BRQfb9l+4yJgLAaaTe6Nu6kpQOXm+7Hr74sQqAvDhAc/BSI9u6E7SKvCXx6aj2ZbEx7+6+coNzV71I4vaZQt5YHFJp/FEHmXEBERdY9CgX53/wlfvj0NKXvXQX/Hb3z3WaII1JUBFeeAqitwGItQXZuPsroi1DZWAI1VAICO7m0Rv/GaKCghaIORljITEd95E2jvzh1rE6wHfo9DOX9EiDoImXPfANK/1/6+bkgMC8Cf7r8fj374EWZt2IhfTJ2HJZP7y+auHI1KAUCExeaAXtPxook9iQULERF1X1M1HIIAi7kOel+0X3oCV3asRGnNFQiW+pZtggqKwAiEB8YiKbQ/hqXcBsFhASyNwJD5gFoPqLSASgc4bC0LOdrNLa831wJNNUDVFew8/Q5uP3cHMOzurz9PFIHzW5G7ew1qzDWYMmIpNNNXAtogrx3SmJQw7H3ih3jp8xN48cuPsflkf7zynduRkRjqtc/w1M0eFrPdDsC9uVx8hQULERF1j7UJ5//1GPqHDYZh/utebdqR+wH+ffAlNDVWIjU4GVNH/wRCzDAgeggQmgoouj+yYUT1JeTufhojB94OaAKBivMo+ewpnCs/gYz4SRg599U26wR5i0Gvxm/uGY/7xwzEf/1zB773f3/E/cNux3/NyXB70jdvutmBZLPLZwwLCxYiIuoW++E/oLTpBmZ9/wNA4d3LB/kn30GyMgCJs14GMu4H1Dqvtg8A0XNfxbm3p8Gy61nYBQGHz7yL2IBo3H73X4BBc7z+ee0ZnRyGz3/2fbx96ArWH9qOzy4dwc+nzsP941KgVvb8cNOcwioEaVWIDfH+f29PcdAtERF5ThSRe+YDjBtwJxCd5t2268pQVH4KiWN/DIz5gU+KFQBAeD9MGf1TfHL+fWSf+xC3TvwFhv3kqx4rVm5SKRX48fSB2PXYjzExfiye3fU3zHz9HWw9WQpHD9+ts+vqCUxIyIBCJmNqAPawEBFRd1ReREP9dYSk3+f1ph0n3oUgKFsGufqYetovcV9AJDB0ARAS7/PP60x8qB7/88BEnCsdiqwdR/HUtr/i1X0xeHT8DNw+NBaxBt/2epwsqsXV6mL8cOw0n36Ou1iwEBGR50pPtPxMGufddusr8O9jGzB68N2APtS7bbdHrQMmejaVva8MjQ/BXx/OxPGCMXjjy+N4bvff8ewuIN4QiGnJo3HrwGRM7B8Bg957Y12OF9Tg4Q/eRVpEGuaPkLZw+zYWLERE5DGx/ByEwGhAZ/Beo001KNp8P/QKFYJmPeu9dnupMSlh+POSTFTVT8ORq1U4cKkc+64dx4dnDkABICpYjwhdFBICExEbEoI4gw6JYQFICtcjOTwAoQFdT27XZLFj89ECvHrwnxgYdgvefXguAjTyKhHklYaIiHoVq9kErSbQew3WFiHv/bthMtdi3Pc/AgIjvdd2LxcRpMVdGfG4KyMewCgUVTfi8JVKXKmoQ1FdOUobLuFcQQOqGyxostqd74sMMCAtMhWDIuPQLzIIqZEBSI0IRKBWhQpTM7afLsGfc3aj0dqEO26ZhJfvmSC7YgVgwUJERN0gQoTgxSXpxOy3UGCuweyHdng8k2xfkRQegPvDk//zbJhzuyiKMDZZUVzThKuVDcgpKsXZimvYdvkCKk9Y4Gh1vgSolSK+O3Q6Hps+FEnhAT16DO5gwUJERB7ThqbAfOlfXmtP0AYiQFCyWOkGQRAQGqBBaIAG6QkGLBgRD2AsgJap9otrGpFf1YBGix0xITrcEhWEsED31kSSAgsWIiLyXFgKYG1omTVWH9b99gQl4LB3vR95RKNSoH9UEPpHeW/G3p7CeViIiMhzYakQAaDqqleaqyw9hnBDilfaIv/CgoWIiDwXPQxQB8B+8bPutyWKyCs7hsHJt3a/LfI7LFiIiMhzah1G9LsdJ89/DDgc3Wur4hzszUYo+8lrwjKSBxYsRETULSGjl8LYUAZc3tmtdsQL2wGVHkid6qVk5E9YsBARUfckT0RK+BBcO/Abz3tZHA6cO/83pCVMBFRa7+Yjv8CChYiIukcQ0P+2Z5FXfR72w3/wrI0zf0eZqRDRk5Z7NRr5j24VLFlZWRAEAStWrHBu27JlC+bMmYPIyEgIgoDc3Nwu29m0aROmTZuGsLAwhIWFITMzE0ePHu1ONCIi6kn9b8WMjKXY8+9XgMu73HtvbSG+2r0aI1NmAimTfJOPej2PC5bs7Gxs3LgRGRkZrbY3NDRgypQpePnll11ua9++fVi0aBH27t2LI0eOIDk5GbNnz0ZJSYmn8YiIqIfpZj2LkXETsPOTH0A88Z5rbzLX48rm+xCk0iPirjd8G5B6NUEU3Z9Tub6+HqNHj8aGDRuwbt06jBw5EuvXr2+1T35+Pvr164cTJ05g5MiRbrVvt9sRFhaGN954A0uWLHHpPSaTCQaDAUajESEhIW59HhEReYndhvptT+LLi//AzIF3I2DMD4HEsYAgtN23/BxKtj2BgtrLmPzgZ0DM0J7PS5Jz9fvbo5luly1bhnnz5iEzMxPr1q3zOGRHGhsbYbVaER4e3uE+ZrMZZrPZ+dxkMnk9BxERuUmpQtCC/8GcI0Nw4tibaMzbgpDgeAzvfwdUcSMBXQjE66dRXpqNsyUHEauPxeTvvc9ihbrkdsGyefNm5OTkIDs72xd5AACrVq1CQkICMjMzO9wnKysLzz33nM8yEBGRhwQBqslPYNzEnwHX9qM29684enkbHCffhghA0AQiOuwWzJyyBorxjwIq+a9jQ9Jzq2ApKirC8uXLsWPHDuh0Op8E+u1vf4sPPvgA+/bt6/QzVq9ejaeeesr53GQyISkpySeZiIjIAwolMGAmQgfMxGQAaKoFzHWAIbH9S0REnXCrYDl+/DgqKiowZswY5za73Y79+/fjjTfegNlshlKp9DjMq6++ipdeegm7du1qM5j327RaLbRa3qtPRNRr6ENbHkQecKtgmTVrFk6fPt1q29KlS5GWloaVK1d2q1h55ZVXsG7dOnzxxRcYO3asx+0QERGR/3GrYAkODkZ6enqrbYGBgYiIiHBur66uRmFhIUpLSwEAeXl5AIDY2FjExsYCAJYsWYKEhARkZWUBaLkMtHbtWrz//vtITU1FWVkZACAoKAhBQb1vCWwiIiLyLq/PdLt161aMGjUK8+bNAwAsXLgQo0aNwh//+EfnPoWFhbh+/brz+YYNG2CxWHDvvfciLi7O+Xj11Ve9HY+IiIh6IY/mYZEjzsNCRETU+7j6/c21hIiIiEj2WLAQERGR7LFgISIiItljwUJERESyx4KFiIiIZI8FCxEREckeCxYiIiKSPRYsREREJHssWIiIiEj23FpLSM5uTthrMpkkTkJERESuuvm93dXE+35TsNTV1QEAkpKSJE5CRERE7qqrq4PBYOjwdb9ZS8jhcKC0tBTBwcEQBMGrbZtMJiQlJaGoqKjPrVPEY+ex89j7Dh47j12KYxdFEXV1dYiPj4dC0fFIFb/pYVEoFEhMTPTpZ4SEhPS5v8g38dh57H0Nj53H3tdIeeyd9azcxEG3REREJHssWIiIiEj2WLC4QKvV4plnnoFWq5U6So/jsfPY+xoeO4+9r+ktx+43g26JiIjIf7GHhYiIiGSPBQsRERHJHgsWIiIikj0WLERERCR7LFiIiIhI9vp8wbJv3z4IgtDuIzs7GwBQVVWFO+64A/Hx8dBqtUhKSsLjjz/e5UKLM2bMaNPmwoULe+KwXOLLYzebzXjiiScQGRmJwMBALFiwAMXFxT1xWC5x5dhPnjyJRYsWISkpCXq9HkOGDMFrr73WZdv+cN49PXZ/OO8AsHz5cowZMwZarRYjR450qW1/OO+AZ8fuL+e9sLAQ8+fPR2BgICIjI/Hkk0/CYrF02ra/nHdPjr3Hz7vYx5nNZvH69eutHo888oiYmpoqOhwOURRFsbq6WtywYYOYnZ0t5ufni7t27RIHDx4sLlq0qNO2b731VvHRRx9t1XZtbW1PHJZLfHnsP/3pT8WEhARx586dYk5Ojjhz5kxxxIgRos1m64lD65Irx/7WW2+JTzzxhLhv3z7xypUr4l//+ldRr9eLr7/+eqdt+8N59/TY/eG8i6IoPvHEE+Ibb7whPvTQQ+KIESNcatsfzrsoenbs/nDebTabmJ6eLs6cOVPMyckRd+7cKcbHx4uPP/54p237w3n39Nh7+rz3+YLl2ywWixgdHS0+//zzne732muviYmJiZ3uc+utt4rLly/3Yjrf8tax19bWimq1Wty8ebNzW0lJiahQKMTPP//ca3m9ydVj/9nPfibOnDmz03389bx3dez+eN6feeYZtwoWfzrvrh67v5z37du3iwqFQiwpKXFu++CDD0StVisajcYO2/KH8+7JsUtx3vv8JaFv27p1KyorK/Hwww93uE9paSm2bNmCW2+9tcv23nvvPURGRmLYsGH45S9/ibq6Oi+m9S5vHfvx48dhtVoxe/Zs57b4+Hikp6fj8OHD3ozsNa4cOwAYjUaEh4d32Z6/nXeg62P35/PuKn88713xl/N+5MgRpKenIz4+3rltzpw5MJvNOH78eKft9fbz7smxS3He/Wa1Zm956623MGfOHCQlJbV5bdGiRfjnP/+JpqYmzJ8/H3/60586bWvx4sXo168fYmNjcebMGaxevRonT57Ezp07fRW/W7x17GVlZdBoNAgLC2u1PSYmBmVlZV7P7Q2dHftNR44cwUcffYRPP/2007b86bzf5Mqx++t5d5U/nndX+Mt5LysrQ0xMTKv9wsLCoNFoOj0Ofzjvnhy7JOfdJ/02MvDMM8+IADp9ZGdnt3pPUVGRqFAoxI8//rjdNq9fvy6eP39e/OSTT8ShQ4eKjz32mFuZjh07JgIQjx8/7vFxuULqY3/vvfdEjUbTZntmZqb4k5/8pHsH1wVfHLsoiuKZM2fEqKgo8YUXXnA7U28+76Lo+rH743l355LQt/X28+7qsfvLeX/00UfF2bNnt/kMtVotfvDBBy5n6o3n3ZNjl+K8+20Py+OPP97lSO3U1NRWz//85z8jIiICCxYsaHf/2NhYxMbGIi0tDREREZg2bRrWrl2LuLg4lzKNHj0aarUaly5dwujRo116jyekPvbY2FhYLBbU1NS0qr4rKiowefJk9w/IDb449nPnzuG2227Do48+ijVr1ridqTefd3eO3d/Oe3f15vPuDn8577Gxsfjqq69abaupqYHVam3T+9CZ3njePTl2Sc67T8qgXsjhcIj9+vUTf/GLX7i0//79+0UA4rVr11z+jNOnT4sAxC+//NLDlL7h7WO/ORjrww8/dG4rLS2V5SC8ro79zJkzYnR0tPj//t//8/gzeut5d/fY/em839SdHpbeet5vcnfQbW8/7zcHnpaWljq3bd68uctBt9/WG8+7J8cuxXlnwfIfu3btEgGI586da/Pap59+Kr799tvi6dOnxWvXromffvqpOGzYMHHKlCnOfYqLi8XBgweLX331lSiKonj58mXxueeeE7Ozs53vSUtLE0eNGiWbW/1u8vaxi2LL7W6JiYnirl27xJycHPG2226T1W2ON3V27DcvhSxevLjVLYEVFRXOffz1vHty7KLoH+ddFEXx0qVL4okTJ8Sf/OQn4qBBg8QTJ06IJ06cEM1msyiK/nveRdH9YxdF/zjvN2/tnTVrlpiTkyPu2rVLTExMbHVrr7+ed0+OXRR7/ryzYPmPRYsWiZMnT273tT179oiTJk0SDQaDqNPpxIEDB4orV64Ua2pqnPtcu3ZNBCDu3btXFEVRLCwsFKdPny6Gh4eLGo1GHDBggPjkk0+KVVVVPXA07vH2sYuiKDY1NYmPP/64GB4eLur1evGuu+4SCwsLfXwk7uvs2Du6RpySkuLcx1/PuyfHLor+cd5FseVW1faO/2avor+ed1F0/9hF0X/Oe0FBgThv3jxRr9eL4eHh4uOPPy42Nzc7X/fn8+7usYtiz593QRRF0TcXm4iIiIi8g/OwEBERkeyxYCEiIiLZY8FCREREsseChYiIiGSPBQsRERHJHgsWIiIikj0WLERERCR7LFiIiIhI9liwEBERkeyxYCEiIiLZY8FCREREsvf/AdYSZL2rfWB9AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Below I plot these again by county no, with faded neighboring counties\n",
      "0   [9, 3]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now finalize the corner lists.  Remember, our avgDistrictPop and normal pop threshold are 781460.625 97682\n",
      "let's decide whether to delete, accept, or modify list [9, 3] with pop 34855.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) [9, 3, 0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, I will replace [9, 3] with [9, 3, 0]\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if you want to manually add another corner-county cluster 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our final pops and fused-county lists are...\n",
      "263851.0 [9, 3, 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#copied 2/17/24 from CO\n",
    "\n",
    "aDP = float(statePop)/nDistricts\n",
    "maxCCBratio = 1./8.  #adjustable max fraction of district pop for corner county blocks.  Let's try 3/16\n",
    "maxCCBpop = maxCCBratio * aDP\n",
    "print(\"continuing from above, develop corner county blocks from each low-pop corner county until it hits\",r3(maxCCBratio),\"of\",int(aDP) )\n",
    "CCBlist, CCBpop, passThruList = list(), list(), list()  #\"waiveThru\" counties get chance to join a cluster even if high pop\n",
    "nFuses = 0\n",
    "for c in has1neighborCs:\n",
    "    print(\"checking if county\",c,\"with pop\",countyPop[c],\"and only 1 neighbor is less pop than\",int(maxCCBpop),\"vs districtPop\",int(aDP) )\n",
    "    if countyPop[c] > maxCCBpop:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "        nc = neighborCountyList[c][0]\n",
    "        plotPoly(countyGeom[nc],0.5)\n",
    "        plotPoly(MAP,0.2)\n",
    "        print(\"county\",c,\"has pop\",countyPop[c],\"and its only neighbor has pop\",countyPop[nc],\"vs districtPop\",aDP,\". Default = keep un-fused\")\n",
    "        print(\"enter 0 to keep\",c,\"as an unfused collection of units, 1 to fuse as one unit and stop, 2 to force-add at least the next closest county\")\n",
    "        fuseChoice = input(\"0, 1, or 2.  Any other input taken as a zero\")\n",
    "        if fuseChoice == 0:\n",
    "            keepUnfused = True\n",
    "            break\n",
    "        elif int(fuseChoice) == 1:\n",
    "            CCBlist.append([c])\n",
    "            CCBpop.append(countyPop[c])  \n",
    "            hasFewNeighborCs.append(c)  #this adds this [c] to the final list of fused units, but will fail to find more in below\n",
    "        elif int(fuseChoice) == 2:\n",
    "            CCBlist.append([c,nc ] )\n",
    "            CCBpop.append(countyPop[c] + countyPop[nc])\n",
    "            hasFewNeighborCs.append(c)\n",
    "            passThruList.append(c)   #pass on thru to the below 2neighbor logic even if too high-pop to qualify\n",
    "    else:\n",
    "        hasFewNeighborCs.append(c)  #normal case; we'll handle in next, more general, for loop            \n",
    "        \n",
    "for c in hasFewNeighborCs:\n",
    "    print(\"checking if county\",c,\"with pop\",countyPop[c],\"and 1 or 2 neighbors is less pop than\",int(maxCCBpop) )\n",
    "    if countyPop[c] < maxCCBpop :\n",
    "        CCBlist.append([c])\n",
    "        CCBpop.append(countyPop[c])\n",
    "        CCBno = CCBlist.index([c])\n",
    "    if c in passThruList:\n",
    "        CCBno = CCBlist.index([c,neighborCountyList[c][0] ])\n",
    "    if countyPop[c] < maxCCBpop or c in passThruList:            \n",
    "        CCBstarter = c\n",
    "        canAdd = True\n",
    "        while canAdd:\n",
    "            nbrSet = set()\n",
    "            for cc in CCBlist[CCBno]:\n",
    "                nbrSet = ( nbrSet.union(set(neighborCountyList[cc])) ).difference(set(CCBlist[CCBno]))\n",
    "            nPop = [countyPop[cc] for cc in nbrSet]\n",
    "            nDist = [countyGeom[cc].centroid.distance(countyGeom[CCBstarter].centroid) for cc in nbrSet]\n",
    "            idx = np.argsort(nDist)\n",
    "            nbrList, newList = list(nbrSet), list()\n",
    "            for i in range(len(nDist)):  #add counties, closest to starter that will fit until over\n",
    "                cc = nbrList[idx[i]]\n",
    "                if CCBpop[CCBno] + countyPop[cc] < maxCCBpop:\n",
    "                    newList.append(cc)\n",
    "                    CCBlist[CCBno].append(cc)\n",
    "                    CCBpop[CCBno] += countyPop[cc]\n",
    "                    #print(\"adding county\",cc,\"with pop\",countyPop[cc],\"to list started by\",CCBstarter,\".Cluster pop now\",CCBpop[CCBno]  )\n",
    "            if newList == list():  #no eligible neighbor counties found; stop\n",
    "                canAdd = False\n",
    "                for cc in CCBlist[CCBno]:\n",
    "                    plotPoly(countyGeom[cc])\n",
    "                    plotCenter(CCBno,countyGeom[cc])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "                    \n",
    "print(\"Below I plot these again by county no, with faded neighboring counties\")\n",
    "plotPoly(MAP,0.15)\n",
    "for L in CCBlist:\n",
    "    print(CCBlist.index(L),\" \",L)\n",
    "    for c in L:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "        for cc in list(set(neighborCountyList[c]).difference(L) ):\n",
    "            plotPoly(countyGeom[cc],0.4)\n",
    "            plotCenter(cc,countyGeom[cc],8)\n",
    "plt.show()\n",
    "rejectList = list()\n",
    "print(\"Now finalize the corner lists.  Remember, our avgDistrictPop and normal pop threshold are\",r3(aDP), int(maxCCBpop))\n",
    "for i,L in enumerate(CCBlist):\n",
    "    if L[0] not in passThruList:  #if we forced the fused-counties list above, don't give option here to modify\n",
    "        print(\"let's decide whether to delete, accept, or modify list\",L,\"with pop\",CCBpop[i] )\n",
    "        isOK = input(\"enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list)\")\n",
    "        if not (isOK == 1 or isOK == str(1)) :\n",
    "            if isOK == 0 or isOK == str(0) :\n",
    "                rejectList.append(L)\n",
    "            else:\n",
    "                notGood = True\n",
    "                while notGood:       \n",
    "                    Lnew = ast.literal_eval(isOK)        \n",
    "                    if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                        notGood = False\n",
    "                    else:\n",
    "                        print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                        isOK = input(\"Try again here \")\n",
    "                print(\"OK, I will replace\",L,\"with\",Lnew)\n",
    "                CCBpop[i] = np.sum( [countyPop[c] for c in Lnew] )\n",
    "                CCBlist[i] = Lnew.copy()\n",
    "for L in rejectList:\n",
    "    del CCBpop[ CCBlist.index(L)]\n",
    "    del CCBlist[CCBlist.index(L)]\n",
    "stillAdding = True\n",
    "while stillAdding:\n",
    "    addMore = input(\"enter 1 if you want to manually add another corner-county cluster\")\n",
    "    if int(addMore) != 1:\n",
    "        stillAdding = False\n",
    "    else:\n",
    "        isOK = input(\"Type in a [county list], where the corner county is first in the list.  Or type 0 to stop\")\n",
    "        if isOK == 0 or isOK == str(0) :\n",
    "            print(\"OK, we'll stop adding corner clusters\")\n",
    "            stillAdding = False\n",
    "        else:\n",
    "            notGood = True\n",
    "            while notGood:       \n",
    "                Lnew = ast.literal_eval(isOK)        \n",
    "                if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                    notGood = False\n",
    "                    for L in CCBlist:\n",
    "                        if set(L).intersection(set(Lnew)) != set(list()) :\n",
    "                            notGood = True\n",
    "                            print(\"OOPS! The following inputted counties are already in\",L,\":\",set(L).intersection(set(Lnew)) )\n",
    "                            isOK = input(\"Try again here \")\n",
    "                    if notGood == False:\n",
    "                        CCBlist.append(Lnew)\n",
    "                        CCBpop.append( np.sum( [countyPop[c] for c in Lnew] ) )\n",
    "                else:\n",
    "                    print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                    isOK = input(\"Try again here \")\n",
    "print(\"Our final pops and fused-county lists are...\")\n",
    "allFusedCounties = list()\n",
    "CCBgeom = [dummyPoly for L in CCBlist ]\n",
    "CCBcp = list()\n",
    "for i, L in enumerate(CCBlist):\n",
    "    CCBcp.append( getHDcp([countyCP[c] for c in L], [countyPop[c] for c in L], [ j for j in range(len(L)) ] ) )\n",
    "    print(CCBpop[i],L)\n",
    "    pops, CPs = list(), list()\n",
    "    for c in L:\n",
    "        if c == L[0]:\n",
    "            CCBgeom[i] = countyGeom[c]\n",
    "        else:\n",
    "            CCBgeom[i] = CCBgeom[i].union(countyGeom[c])\n",
    "        allFusedCounties.append(c)\n",
    "        pops += countyPop[c]       #  IS THIS EVEN USED?\n",
    "        CPs.append(countyCP[c])   #  IS THIS EVEN USED?\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(i,countyGeom[c])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "206c9372-8ec8-4190-93ed-04df286466e0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now identify the border counties + border fused units, and interior small counties with pop < 15629\n",
      "We defined a total of 0 unit (but not corner-cluster) counties in the map, excluding the cut-out districts\n"
     ]
    }
   ],
   "source": [
    "maxWholeBorderCountyPop = 0.02 * aDP   #to encourage contiguity, border counties > 0.02 aDP will be forced whole\n",
    "maxInteriorBorderCountyPop = 0.02 * aDP\n",
    "print(\"now identify the border counties + border fused units, and interior small counties with pop <\", int(maxInteriorBorderCountyPop))\n",
    "unitCounties, borderCounties = list(), list()\n",
    "for c in uncutCountyList:\n",
    "    if countyGeom[c].intersects(MAP.exterior):\n",
    "        borderCounties.append(c)\n",
    "        if c not in allFusedCounties:\n",
    "            if countyPop[c] < maxWholeBorderCountyPop:\n",
    "                unitCounties.append(c)\n",
    "    else:\n",
    "        if countyPop[c] < maxInteriorBorderCountyPop:\n",
    "            unitCounties.append(c)\n",
    "print(\"We defined a total of\",len(unitCounties),\"unit (but not corner-cluster) counties in the map, excluding the cut-out districts\")        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e0ff719c-59dc-4827-a522-11bd352f7524",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For this state, do we have a lot of populated fragmented VTDs?\n",
      "4 fragmented VTDs out of 2157\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FYI, here are the locations of fragmented VTDs with pop > 3000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"For this state, do we have a lot of populated fragmented VTDs?\")\n",
    "nFragmentedVTDs,fragmentedVTDs = 0, list()\n",
    "for v in populatedTractList:\n",
    "    c = countyNo[v]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :  #a multipoly vtd-based unit\n",
    "            nFragmentedVTDs +=1\n",
    "            fragmentedVTDs.append(v)\n",
    "print(len(fragmentedVTDs),\"fragmented VTDs out of\",nVTDs)\n",
    "fragPop = [tractPop[v] for v in fragmentedVTDs]\n",
    "plt.hist(fragPop)\n",
    "plt.show()\n",
    "print(\"FYI, here are the locations of fragmented VTDs with pop > 3000\")\n",
    "for v in fragmentedVTDs:\n",
    "    if tractPop[v] > 3000:\n",
    "        plotPoly(vtdGeom[v])\n",
    "plotPoly(MAP,0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "71d771f8-dbe2-48f9-a702-9f66a62d1174",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now writing the master list of units, which may be individual vtd's (+surrounds), whole counties, or corner county clusters\n",
      "I split up 4 fragmented VTDs. Now define unit neighbor lists and fuse geometries of surrounds.\n",
      "looking for surrounds, now working on vtd 0\n",
      "looking for surrounds, now working on vtd 502\n",
      "looking for surrounds, now working on vtd 1194\n",
      "looking for surrounds, now working on vtd 1716\n",
      "After surround checks, we appear to have 1859 building blocks defined. We captured 2 surrounded VTDs\n",
      "working on unit neighbor lists for county 0\n",
      "All done creating unit lists\n"
     ]
    }
   ],
   "source": [
    "#3/2/24 - split up all multipoly VTDs into fragments, divvy pop by area\n",
    "unitPop, unitCP, unitGeom, nbrUnitGeom, allUnits = list(), list(), list(), list(), list()\n",
    "vtdUnits, countyUnits, borderUnits = list(),list(),list()\n",
    "countyUnitList = [list() for c in range(nCounties) ]\n",
    "surroundedVTDs, surrounders = list(), list()  #vtds that are surrounded by another vtd - problem for later enclaves, xfer their pops to surrounders\n",
    "#These surrounded VTDs don't get transferred to the unit list\n",
    "print(\"Now writing the master list of units, which may be individual vtd's (+surrounds), whole counties, or corner county clusters\")\n",
    "for c in unitCounties:\n",
    "    unitCP.append(countyCP[c])\n",
    "    unitPop.append(countyPop[c])\n",
    "    unitGeom.append(   countyGeom[c] )\n",
    "    nbrUnitGeom.append(countyGeom[c] )\n",
    "    countyUnits.append(c)\n",
    "    allUnits.append(c+0.5)  #use non-integers to avoid vtd and county and cluster confusion\n",
    "\n",
    "nVTDneighbors = [0 for v in range(nVTDs)] #this loop -- just checking for surrounded VTDs\n",
    "lastVTDneighbor = [-999 for v in range(nVTDs)]\n",
    "\n",
    "nFragmentedVTDs,fragmentedVTDs, coastalFragmentedVTDs = 0, list(), list()  #a prev version treated coastal separately\n",
    "fragVTDgeom, parentVTDno, fragVTDpop = vtdGeom.to_list(), [v for v in range(nVTDs)], tractPop.to_list()  #these lists will expand to include vtd fragments \n",
    "origVTDgeom = vtdGeom.copy() #we create a parallel fragVTDgeom list which grabs the 1st fragment, then append fragments to the total list\n",
    "origVTDpop = tractPop.copy()\n",
    "VTDchildren = [[v] for v in range(nVTDs)]  #the list of all fragment numbers associated with the original VTD, including ones that get surrounded\n",
    "countyFragVTDset = [set(countyTractList[c]) for c in range(nCounties)]\n",
    "for v in populatedTractList:\n",
    "    c = countyNo[v]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :  #a multipoly vtd-based unit\n",
    "            nFragmentedVTDs +=1\n",
    "            fragmentedVTDs.append(v)\n",
    "            geos, areas = [geo for geo in vtdGeom[v].geoms ], [geo.area for geo in vtdGeom[v].geoms]\n",
    "            for i, geo in enumerate(geos):\n",
    "                if i == 0:\n",
    "                    fragVTDgeom[v] = geo\n",
    "                    fragVTDpop[v] = tractPop[v] * areas[i] / np.sum(areas)  #overwrite, then distribute balance below\n",
    "                else:\n",
    "                    fNo = len(fragVTDgeom)\n",
    "                    fragVTDgeom.append(geo)\n",
    "                    fragVTDpop.append( tractPop[v] * areas[i] / np.sum(areas) )\n",
    "                    parentVTDno.append(v)\n",
    "                    countyFragVTDset[c].add(fNo )\n",
    "                    VTDchildren[v].append(fNo)\n",
    "                    nVTDneighbors.append(0)\n",
    "                    lastVTDneighbor.append(-999)\n",
    "                    \n",
    "print(\"I split up\",nFragmentedVTDs,\"fragmented VTDs. Now define unit neighbor lists and fuse geometries of surrounds.\")\n",
    "\n",
    "debugV = -7616\n",
    "for kk,V in enumerate(populatedTractList):\n",
    "    if kk%500 == 0:\n",
    "        print(\"looking for surrounds, now working on vtd\",V)\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties:\n",
    "        for v in VTDchildren[V]:\n",
    "            for vv in countyFragVTDset[c].difference({v}) :\n",
    "                if fragVTDgeom[vv].intersects(fragVTDgeom[v]):\n",
    "                    nVTDneighbors[v] +=1\n",
    "                    lastVTDneighbor[v] = vv\n",
    "                    if v == debugV:\n",
    "                        print(\"I just added neighbor\",vv,\"to magicV\",v)\n",
    "                #if nVTDneighbors[v] >= 4:  #Enough to have >=2 even after surrounds.  Will do full neighbor list later\n",
    "                #    break\n",
    "            if nVTDneighbors[v] < 4:  #OK if a vtd has only 1 intracounty neighbor IF it touches another county, it's not surrounded\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if fragVTDgeom[v].intersects(countyGeom[cc]):\n",
    "                        if cc not in unitCounties + allFusedCounties:\n",
    "                            for vv in countyFragVTDset[cc] :\n",
    "                                if fragVTDgeom[vv].intersects(fragVTDgeom[v]):\n",
    "                                    nVTDneighbors[v] +=1\n",
    "                                    lastVTDneighbor[v] = vv\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                print(\"WARNING. vtd frag\",v,\"in county\",c,\"intersected county\",cc,\"but had no intracounty neighbors\")\n",
    "                                plotPoly(fragVTDgeom[v],2)\n",
    "                                plotCenter(\"iso\",fragVTDgeom[v])\n",
    "                                plotPoly(countyGeom[cc])\n",
    "                        else:\n",
    "                            nVTDneighbors[v] +=1\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                raise Exception(\"neighborless vtd fragment\",v,\"neighbors a unit or fused county\",cc)                                \n",
    "            if v == debugV:\n",
    "                print(v,\"has\",nVTDneighbors[v],\"neighbors.  its last is\",lastVTDneighbor[v])\n",
    "                \n",
    "for loop in range(3): #to catch surrounds of surrounds\n",
    "    for V in populatedTractList:\n",
    "        c = countyNo[V]\n",
    "        if c not in allFusedCounties and c not in unitCounties: \n",
    "            for v in VTDchildren[V]:\n",
    "                if nVTDneighbors[v] == 1:\n",
    "                    vv = lastVTDneighbor[v]\n",
    "                    if v in surrounders:  #transferring surrounded of surrounds to master surrounder\n",
    "                        for sNo, s in enumerate(surroundedVTDs):\n",
    "                            if surrounders[sNo] == v:\n",
    "                                surrounders[sNo] = vv\n",
    "                    surroundedVTDs.append(v)\n",
    "                    surrounders.append(vv)\n",
    "                    nVTDneighbors[vv] -=1\n",
    "                    nVTDneighbors[v] -=1 #in case this surrounder becomes a later surroundee\n",
    "                    fragVTDpop[vv] += fragVTDpop[v]\n",
    "                    fragVTDpop[v] = 0\n",
    "                    if lastVTDneighbor[vv] == v:  #find another neighbor in case this surrounder is also a surroundee in loop 2\n",
    "                        for vvv in countyFragVTDset[c]:\n",
    "                            if vvv not in [v,vv]:\n",
    "                                if fragVTDgeom[vvv].intersects(fragVTDgeom[vv]):\n",
    "                                    lastVTDneighbor[vv] = vvv\n",
    "                    if lastVTDneighbor[vv] == v:\n",
    "                        print(\"WARNING.  We did not find another neighbor of surrounder\",vv,\"other than surroundee\",v)\n",
    "                    if loop > 0:\n",
    "                        print(\"On loop\",loop+1,\"for county\",c,\" we found that vtd frag\",v,\"now has only 1 neighbor\",vv)\n",
    "            \n",
    "for V in populatedTractList:\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        for v in VTDchildren[V]:\n",
    "            if nVTDneighbors[v] >1:\n",
    "                unitCP.append(fragVTDgeom[v].centroid)\n",
    "                unitGeom.append(fragVTDgeom[v])\n",
    "                unitPop.append(fragVTDpop[v])   #for now, ratio pop by area, not block decomposition\n",
    "                vtdUnits.append(v)   #if a border unit, we'll catch in below big loop, after checking for surround\n",
    "                allUnits.append(v)\n",
    "#for V in populatedTractList:\n",
    "#    c = countyNo[V]\n",
    "#    if c not in allFusedCounties and c not in unitCounties:  \n",
    "#        for v in VTDchildren[V]: \n",
    "for V in populatedTractList:\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        for v in VTDchildren[V]:\n",
    "            if nVTDneighbors[v] == 1:  #a surrounded VTD, transfer its pop to surrounder unit.  Don't bother with shifting centerpoints\n",
    "                print(\"somehow we missed 1-neighbor vtd frag\",v,\" with last vtd frag nbr\",lastVTDneighbor[v])\n",
    "            if nVTDneighbors[v] == 0 and v not in surroundedVTDs:\n",
    "                print(\"WARNING. vtd\",v,\"had no found neighbors\")\n",
    "\n",
    "for i, L in enumerate(CCBlist):\n",
    "    unitCP.append( CCBcp[i] )\n",
    "    unitPop.append(CCBpop[i])\n",
    "    unitGeom.append(   CCBgeom[i])\n",
    "    nbrUnitGeom.append(CCBgeom[i])\n",
    "    allUnits.append(i+0.25)  #use alt non-integers to avoid vtd and county and cluster confusion\n",
    "\n",
    "nUnits = len(allUnits)\n",
    "print(\"After surround checks, we appear to have\",nUnits,\"building blocks defined. We captured\",len(surroundedVTDs),\"surrounded VTDs\")\n",
    "\n",
    "unitNbrs = [list() for u in range(nUnits)]\n",
    "countyUnitList = [list() for c in range(nCounties)]\n",
    "for c in uncutCountyList:\n",
    "    for v in countyFragVTDset[c]:\n",
    "        if v in allUnits:\n",
    "            countyUnitList[c].append(allUnits.index(v))\n",
    "\n",
    "sketchyList = list()\n",
    "for c in uncutCountyList:\n",
    "    if c%20 == 0:\n",
    "        print(\"working on unit neighbor lists for county\",c)\n",
    "    if c not in allFusedCounties:  #see a separate loop below for cluster-cluster neighbors\n",
    "        if c in unitCounties:    \n",
    "            u = allUnits.index(c+0.5)\n",
    "            for cc in neighborCountyList[c]:\n",
    "                if cc not in allFusedCounties:\n",
    "                    if cc in unitCounties:\n",
    "                        unitNbrs[u].append(allUnits.index(cc+0.5))  #we'll catch the complement in the cc county's loop\n",
    "                    else:\n",
    "                        for uu in countyUnitList[cc] :\n",
    "                            if countyGeom[c].intersects(unitGeom[uu]):\n",
    "                                unitNbrs[u].append(uu)\n",
    "                                unitNbrs[uu].append(u)\n",
    "            for i,geo in enumerate(CCBgeom):\n",
    "                if geo.intersects(countyGeom[c]):\n",
    "                    unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                    unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "        else:  #non-unit county      \n",
    "            for u in countyUnitList[c] :\n",
    "                for uu in list( set(countyUnitList[c]).difference({u}) ) :\n",
    "                    if unitGeom[u].intersects(unitGeom[uu]):\n",
    "                        unitNbrs[u].append(uu )  #will catch complement later in this county's loop\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if cc not in allFusedCounties and cc not in unitCounties: #see an above block for handling unitCounties\n",
    "                        for uu in countyUnitList[cc]:\n",
    "                            if unitGeom[u].intersects(unitGeom[uu]):\n",
    "                                unitNbrs[u].append(uu )\n",
    "\n",
    "            for u in countyUnitList[c] :\n",
    "                for i,geo in enumerate(CCBgeom):\n",
    "                    if geo.intersects(unitGeom[u]):\n",
    "                        unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                        unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "                if len(unitNbrs[u]) == 0:\n",
    "                    print(\"ERROR - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has no neighbors\")\n",
    "                if len(unitNbrs[u]) == 1:  #after fragmentation, the piece we selected is surrounded.  KISS - take on this neighbor's neighbors                       \n",
    "                    print(\"WARNING - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has only 1 neighbor=\",unitNbrs[u],\". Optional triage in next block\")\n",
    "                    sketchyList.append([u,unitNbrs[u][0] ] )\n",
    "\n",
    "CCBunits = CCBlist.copy()                        \n",
    "for i, geo in enumerate(CCBgeom):  #this loop: only cluster-cluster intersections.  Cluster-vtd and cluster-county neighbors already found above.\n",
    "    for ii in range(i+1,len(CCBgeom) ) :\n",
    "        if geo.intersects(CCBgeom[ii]):\n",
    "            unitNbrs[allUnits.index(i+0.25) ].append(allUnits.index(ii+0.25) ) #all CCB-county, CCB-vtd intersxns already covered\n",
    "            unitNbrs[allUnits.index(ii+0.25)].append(allUnits.index(i+0.25) )\n",
    "print(\"All done creating unit lists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3ed5ebb3-f0ab-42c1-b0f1-34ebf9124dc0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the neighbors for unit 785 in county 8\n"
     ]
    },
    {
     "data": {
      "image/png": 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KDGsWoap5954s/eFbTMNAKUVxfp7VIQkRVKqUsEyePJkuXbrgdrtxu92kp6czc+ZMAHw+H3fffTedO3cmOjqalJQURo0axbZt24553c8++4wOHTrgcrno0KED06dPP77SiDrP9EsNiwhN9ZJTaN+3Pz9/+DYbMhfyxi3Xkrttq9VhCRE0qpSwpKamMnHiRBYuXMjChQsZMGAAw4cPZ/ny5RQXF7N48WIeeOABFi9ezLRp08jKyuK888476jX/+OMPLr30UkaOHMmff/7JyJEjueSSS5g/f/4JFUzUTaZhyEy3ImS169OPiKhofpv6PnFJDXFGRFgdkhBBQ1NKqRO5QEJCAk8//TTXXHPNIfsyMjLo1asXmzZtokmTJoc9/9JLLyU/P7+8pgbgrLPOol69evzvf/+rdBz5+fnExcWRl5eHWzqu1Vl/zv6G2MQGtOh+stWhCFFl2WtW8/fKZaz8dS5RcfEkt2xN3o7teEuKadSqLb0vHGF1iEJUu8p+fh/3V1HDMPjkk08oKioiPT39sMfk5eWhaRrx8fFHvM4ff/zB7bffXmHbkCFDeP7554/6+h6PB4/HU/48Pz+/0rGL8BWYh0WGNYvQ9MPbr7J93Vpa9OhFz3POxxkVhbtBEso0+fQ/D3DKPy5B06XroaibqpywLF26lPT0dEpLS4mJiWH69Ol06NDhkONKS0u55557uPzyy4+aMeXk5NCwYcMK2xo2bEhOTs5R45gwYQKPPPJIVcMXYc70+9Ht0ulWhKYrHn/2kG1rM+Yxf/pHpLRtz5Jvv6ZjvwGyxpCok6qcqrdt25bMzEzmzZvH2LFjGT16NCtWrKhwjM/nY8SIEZimycsvv3zMa2qaVuG5UuqQbQcbP348eXl55bctW7ZUtSgiDBnSh0WEmeRWbYhr2IjIWDex9evzxTOP4y0tsTosIWpdld/ZnU4nrVq1AqBnz55kZGQwadIkXn31VSCQrFxyySVs2LCBH3744Zj9SZKTkw+pTdmxY8chtS4Hc7lcuFyuqoYvwpyMEhLhJqZeAueOu6v8uc3uIOOLT+lz6UgLoxKi9p1wY6hSqrwvyb5kZc2aNcyZM4f69esf8/z09HRmz55dYdt3333HqaeeeqKhiTrINGQeFhE+vCXFvHvnzSyeOQMAw+9j+dw5NG7X0eLIhKh9VUpY7r33Xn755Rc2btzI0qVLue+++5g7dy5XXHEFfr+fiy66iIULF/LBBx9gGAY5OTnk5OTg9XrLrzFq1CjGjx9f/nzcuHF89913PPnkk6xatYonn3ySOXPmcNttt1VbIUXdYRiG1LBYaMKECWiaVuH/7/bt27nyyitJSUkhKiqKs846izVr1lgXZIjYsmIp0yY+zCkXXMraBX+w6a9M/l6xnMSmzWjW9SSrwxOi1lXpnX379u2MHDmS7Oxs4uLi6NKlC7NmzWLw4MFs3LiRGTMC3wK6detW4bwff/yR/v37A7B582b0A3q5n3rqqUydOpX777+fBx54gJYtW/LRRx9xyimnnFjJRJ0U6HQrCYsVMjIyeO211+jSpUv5NqUU559/Pg6Hgy+++AK3282zzz7LoEGDWLFiBdHR0nn0SLatXslpl19F47btad69J79OfZecNVmcPPxCq0MTwhInPA9LsJB5WATA92++wsnDLsDdIMnqUOqUwsJCTjrpJF5++WX+85//0K1bN55//nmysrJo27Yty5Yto2PHQDOGYRgkJSXx5JNPcu2111ocefD68tkJnHHVDcTUS7A6FCFqVGU/v2VAvwgrpt+PzSHzsNS2m266iXPOOYdBgwZV2L6vf1vEATO22mw2nE4nv/76a63GGEr+nP0NiU2aER1fj+w1q1m/OIMw+W4pxHGTunMRVgy/T5qEatnUqVNZvHgxGRkZh+xr164dTZs2Zfz48bz66qtER0fz7LPPkpOTQ3Z2tgXRhob1izOIrpfAp48/QMMWrfCVluApKqT9aWdYHZoQlpF3dhFWDBnWXKu2bNlS3nE+4jDr3jgcDj777DOuueYaEhISsNlsDBo0iKFDh1oQbegYdse9lBbkExUXj26zUZyfx9x3XpeERdRp8s4uworh82GzS5NQbVm0aBE7duygR48e5dsMw+Dnn3/mv//9Lx6Phx49epCZmUleXh5er5cGDRpwyimn0LNnTwsjD252h4OYhP3TQmzMXERapy5HOUOI8CcJiwgrhszDUqsGDhzI0qVLK2y76qqraNeuHXfffTe2A34XcXFxAKxZs4aFCxfy2GOP1WqsoWzn5o2069PP6jCEsJQkLCLsHGtZB1F9YmNj6dSpU4Vt0dHR1K9fv3z7J598QoMGDWjSpAlLly5l3LhxnH/++Zx55plWhByS8nbksH39Who0bYauS0Iu6iYZJSSEqFHZ2dmMHDmSdu3aceuttzJy5Ej+97//WR1WSDnn1rswDYPPn3wU/wETcQpRl8g8LCKsfP70Y5x/5wNWhyFEjVg2dw7KNOk8QGqnRPiQeVhEnRQm+bcQh9Xu1NNZ9dtcfF6P1aEIUeskYRFCiBBhdzrpNfwSPn/yEdYtmi8JuqhTpNOtEEKEkKZdutGodRsWfzODzO++odd5F5LWUYY8i/AnCYsQQoQYZ2QUvS8cQUlBPj++8zqlxUW0Pjnd6rCEqFHSJCSEECEqMtbNmTfcSuasL/GWFFsdjhA1ShIWIYQIYXaHg94XXsYXz/yHxTO/xFNcZHVIQtQISViEECLEpXXozAXjHyWmXj0+f+ox9mRvtTokIaqd9GERQogwYLPbadO7L3aniw2Zi6jXqLHVIQlRraSGRYQNZZoyLb+o85p06srajHkU7N5V6XNMw8Dw+2owKiFOnNSwiLBhGAY2m/xJi7rN7nQy+Pqbmf3ai0TXq0/DFq1IadOOBk2bHzahV0rx6eMPYBoG/7j7QYrz9hIR6yYyJtaC6IU4Mnl3F2HD8PnQ7fInLUS95BQuGP8IBbt3sWPjOlb+OpdfPnybiJhYGrfrSFqHztRPTQNg99+badCkGa179+GrSU9heL0ktWhF/5HXWFwKISqSd3cRNgy/D5vDYXUYQgSN2PqJxNZPpGWPUwAoKchn66oVLJkVGE00ZMw41i1aQMuevUlo1BhvSQmx9RM5edgFFkcuxKEkYRFhw/D7sEkNixBHFBnrptXJvWl1cm82LFnIzP/+HwAte/RC03XiGyYz9KY7LI5SiMOTd3cRNnTdhmmYVochREho3r0nrugYivL2kJjWlO0b1lkdkhBHJaOERNiIiImltDDf6jCECBkpbdrR+uR0fF4Pnz3xIO369KM4P08WVRRBSWpYRNgwTQNdRgkJUWUOp4shY8aRszaL1b//QnH+XiJiYul9wQgSUmQ+FxEc5N1dhA1lGOg2m9VhCBGSWvboRcsevcqf783J5teP3sNbXIRhGDTp1JVTzr/YwghFXScJiwgbpmGi69LKKUR1iE9uxLnj7kKZJrnbtrLwq+lWhyTqOElYRNgwTQNNaliEqBZ+r5dvX5lEcss2rFnwG2ffcqfVIYk6Tr6OirChTFOahISoJst/mkPTzt3YtWUTTTp1IyYhweqQRB0nCYsIG6ZhSJOQENXEGRXNpqWZ5O3IQZkGnz3+AAu/mo7fJ2sOCWtIk5AIG8o00XSpYRGiOrTv04/EtKbENUjCGRmFaRpkzfuN6RMfol3f/rTv0x+702l1mKIOka+jImyYMkpIiGrVoEkznJFRQGBixnanns75dz0IwPdvTrYyNFEHScIiwkZgHhZJWISoSQ5XBBFRMUTH17M6FFHHSJOQCBuBJiHJwYWoaRv+XIS3pIS1GfMoLSygUeu21E9tYnVYIsxJwiLCRqDTrdSwCFHTzrz+Fgpzd7Psx9msyfiDwtzdkrCIGicJiwgbgT4sUsMiRG1YPOtL8rbncPKwC2ibfprV4Yg6QBIWETZM05AmISFqyZ5tf3PubXdjszusDkXUEfLuLsKGMk1pEhKilrRJP43Zr79EYe5uq0MRdYQkLCJsyLBmIWpP+z796NR/EN+/+YrVoYg6QhIWETZMQ0YJCVGbkpq3tDoEUYfIu7sIG7KWkBC1yxkRSVRcHAu++JSSwgKrwxFhrkoJy+TJk+nSpQtutxu32016ejozZ84s3z9t2jSGDBlCYmIimqaRmZl5zGv6fD4effRRWrZsSUREBF27dmXWrFlVLogQpinDmoWobYOuuZF6ySl8/uSjFOfnAZCzbg0Fu3dZHJkIN1UaJZSamsrEiRNp1aoVAO+88w7Dhw9nyZIldOzYkaKiIvr06cPFF1/MddddV6lr3n///bz//vu8/vrrtGvXjm+//ZZ//OMf/P7773Tv3r3qJRJ1lmnIKCEhapum67Q+5VQiY9189fyT2Ox23A2SKC0qYthtd1sdnggjmlJKncgFEhISePrpp7nmmmvKt23cuJHmzZuzZMkSunXrdtTzU1JSuO+++7jpppvKt51//vnExMTw/vvvVzqO/Px84uLiyMvLw+12V7kcIvStzZhHSUE+nQecaXUoQtRJxfl5OCOj+HvlMnZt2kDPYRdYHZIIAZX9/D7ueVgMw+CTTz6hqKiI9PT0470MHo+HiIiICtsiIyP59ddfj3mex+Mpf56fn3/cMYjwIGsJCWGtKHccADs2rKNR67YWRyPCTZXrz5cuXUpMTAwul4sxY8Ywffp0OnTocNwBDBkyhGeffZY1a9ZgmiazZ8/miy++IDs7+6jnTZgwgbi4uPJbWlracccgwoM0CQkRHHZsXE9SsxZWhyHCTJXf3du2bUtmZibz5s1j7NixjB49mhUrVhx3AJMmTaJ169a0a9cOp9PJzTffzFVXXYXtGN+Ux48fT15eXvlty5Ytxx2DCA+BieMkYRHCSmsXzgelcEVFWx2KCDNVfnd3Op20atWKnj17MmHCBLp27cqkSZOOO4AGDRrw+eefU1RUxKZNm1i1ahUxMTE0b978qOe5XK7y0Ur7bqJuk4njhLCepmko02TXlk3sydmGt6TY6pBEmDjhtYSUUhX6khyviIgIGjdujM/n47PPPuOSSy454WuKusU0DTRJWISwVMsevYiKi2PZ3Dko02TrquUMuuZGklu1sTo0EeKqlLDce++9DB06lLS0NAoKCpg6dSpz584tnzclNzeXzZs3s23bNgBWr14NQHJyMsnJyQCMGjWKxo0bM2HCBADmz5/P1q1b6datG1u3buXhhx/GNE3uuuuuaiukqBukSUiI4NCoVVs2L/2TratXkJjWlKi4eKtDEmGgSgnL9u3bGTlyJNnZ2cTFxdGlSxdmzZrF4MGDAZgxYwZXXXVV+fEjRowA4KGHHuLhhx8GYPPmzRU+VEpLS7n//vtZv349MTExnH322bz33nvEx8efYNFEXWMasvihEMEiZ90aTjp7OE07dZXO8KJanPA8LMFC5mERi2d+SULjVJp1kQkHhbBaSWEBGTM+ozhvL2eNvc3qcEQQq+znt6S9ImxIk5AQweO7V14g44tP8RYXs/GvJeTtyLE6JBHiTrjTrRDBQtYSEiJ4DLxmLB37DcTw+9m87E/+/O4bhv/7PqvDEiFMvo6KsGEaMkpIiGARUy+BVif3plnXk9i2egV9LxsFBL5YmIZxyPGmYeD3+Wo7TBFCpIZFhA1lGNIkJESQsdntxNZvwPxpH9GsWw/+/O4b4pIacvYt/y4/xjQNPpvwEJqmcdF9j1kYrQhmkrCIsGGapkwcJ0SQsTudnHPrneTv2sHfK5fT+4JL2bz8LwAKc3ez4ItP2ZO9lY6nD2DV7z9bHK0IZpKwiLChTFlLSIhg5U5MosNpSRTs3sVvH39AxozPWLPgdwZePZaGLVqhlGLdogX8OvU9el84ArvDYXXIIsjIu7sIG6Y0CQkR9GLrJ3LhvY+Q3LI1w24fT8MWrYDAlP7n3nY3e3K2kbtV1oYTh5IaFhE2TNOUTrdChIDIWDdpHbscsn3X5o3k79xOfMNkC6ISwU6+joqwoUxZ/FCIUFYvJZW26afx6eMPUJi72+pwRJCRhEWEDZmaX4jQZnc46HHO+cQmJB526LOo2yRhEWHDNE3pdCtECFNKMffdN0jt0Al3gySrwxFBRt7dRdhQhjQJCRHKlsz6ktiE+nQ/a5jVoYggJAmLCBum9GERImT9vWo5WfN+5aSzh1sdighSkrCIsKGkSUiIkOQpLuKHKZM5d9zd8qVDHJG8u4uwEZiHRd7shAg1zsgoWvVK54/P/kf+zh1WhyOClCQsImwYfgNPsQdPsQe/349pmpU6r7LHCSFqhqZpnHrxFbTv05+vJj1J3o4cq0MSQUgmjhNhw1PakKkPP4WGwlQmqCMlIhqgyh8X523FFVUfm90V2FVGlR2iaYHjAvcqcL4W2K5pOmg6mq6jaQfcdK3s3oamBR6fNPRM2vftViNlFyIcpHboRLs+/Vk2dw6nXnS5NPGKCiRhEWGjXuNTOOfmy4mMdVbrdZVSKFNhGgrTVGXbAvO+KMPENA0Mv4FpGCjDxDBNTGPfc4M/ps1kx/oVNGyZWq1xCREO9hbm8vIL4zA27yW5czvOPn8My778iuy1WaS0aWd1eCKISMIiwoZpmOg27dgHVpGmaWg2jap2j/F7fXz02CQ0TeO6/z6D3Wn9Ym6mqcjYmEux10DTQNc0bLpW4bGuBcps0zR0LbAvsP2AfWXH6ZqGfuBj7Sjb9YrHaFr1/66E9fIK9/DelAcoKi0kMiKKop178Hu8OOOjiY1yg6Eo9ZVQvDcf02+godFr8HmccedlfDbrv7z51L8YMngURXtyrS6KCDKSsIiwYRoK3RY8VciLZ/7Knm3rsTsjmTJu/MEtUShV1gJVpc/tAy9ScZtSZUlAWRNUoClKCzRZaRqarlNQ6mdncTH5di8KDaVpgIYCVFk712Eflx9L+XkKrew4MNExNB1T6ZiahokNU9Mx0DHRMDUbBoHthqZjomNqOqZmBz3QrLavaQ1NQ9f1wEKWug2broOuY7MF9tv0wL1us5UfZ7PZ0G06um4LPNa1wHk+D0UFBRQXFlFaXITP4wn8fMqa8NBtZY812PeaWuBaaBqaTUcve65ptkDiqtkC23UdzWZD0wKx6bqOTQ8keoGk7sBETyt/vG+7plVM6Oy6RpTTht2mlyeRtvJk79BE8OB9+65pOyAh3HevUfZjLvt96WXbtAMe2206TruOs+zeZa/43GnXsZe9xtF8/c0rmDqcPWwMe/J30rJVFxLdDdmweSXb9mxCs9upF12fpsmtqedOrHDuZcP+xYceH9/88A5n33RbVf5jiDpAEhYRNgIJS/B8a+81/Ax6DT+jVl/TNM3AzV/WLGWqQNOUqTANkw/nbUCbuZbZUdC2YQzKNFFKgQo0ZaEUSpmBpq+yffuOUcpEmSaUHYcyy/oKKTANdGWgKRNdGdgwsSmTstQEp+kte6zQlVF2b2LTTHRlBlImFUiB9PLHgVRKK9u+b/+B27QDtqEOOh4wdBsRuhO37sCnOzC1QEKrVbiWWXZ/4PnmAccASgVSrwOurR1mW1kPp6NSgO+QrRoair1HOOdwaer+7ftf8cCk8sB7tIOOQytLGHVUWfKoNK0s2Qw8VmWJZWC7jh87hqaDbkezOdBsdpTNgW6zo+x+6tUr4qSGipx58zhjxGg6d+hdIdZ2rbrTju7H+OnA5RfdQ/HwYu79/laKKaZ/y3OOeY6oGyRhEWEj2BIWK5TXTBzhf/binX5S9Ej+cWoTnrjg0NVyq4NSCsNUmApMpcpugcfKBKN8m8I0OeSxoQJ9hfzmvusEHu/bVr5PKQyj7P7AYw/YpgHxUQ7io5zUi3ISE2FHlb2WofafY+57fMD2feUwDjjeNA84VimM49hulm03VKBWTCmFz1QUe/yB8h34emXHHBhnxZ/ZvmubgaS07LFpmmXXDmxXAGr/NpRCmQaaaaCZJigDw+/H8Pvx+/yYhh/D8GP6DVAmNmVgV37sysDuN7D5S7FhBp5rBtj3sMdcyZxdLjr2H8+pp/Q9ob+hKEcUTw+ezL0/3IphGgxsfV51/GmKECcJiwgr0i/iyP7eU0zRsmxWOP1M6Nuixl5H0zTsdTxxDCd+w8RrmHj9gZvHX/G51zAp8vh56/e1/L7jS/7c9DaNf2vMtaed2N+Yw+bgiYEv8PCP/2Zb4Vb+2W2M/P+u44KnwV+IE6UOV2kuALbtLeGqF3+lrVfD1qIhrZJirA5JhAi7TSfKaSc+ykmSO4K0hChaNoihfSM3XdPiOblZAv3bJvHOVadyU4+r0HQv36xYWy2v7dAd/GfA8ximn7EzR/PxX1PYkr8l0Iwp6hypYRHhQ759HdHpE35gZJHJ55E6X1x27H4EQlSV3zBZsyMfzVaETau+jxZN07iyxy1c4ivm143f8c7iF9lavB23I5pbTxlPY3datb2WCG6SsAgR5rLzSmjsh+12L8TGYsi3U1HN8kp8XPvu7yz1vI5ReDI3X9S52l8jyhHFma3P58zW5wMwZ/1M5mR9xuiet1X7a4ngJE1CQoS5RnGRRDSKxqVsnLfDy7gnf+BfUzMp8vitDk2EgZy8Ui589TuW+V/AXnwKL59/Nf3aNKjx121bvz2/5izgvSWTKfGX1PjrCetpKkwaA/Pz84mLiyMvLw+32211OMICX7/8F+fcWDMjX0Jdqc9g4cY9TP5+DZGrcmjkt7OqfQJvX3sK0S6paBXHZ3VOAaPe/Zr8mP8RVXAR740cToeU2nv/NZXJ16s+4sfNP1JqenHbA81EKW6ZVTqUVPbzW96phKgDIhw2+rZOpE+r+ny/cgcTpyyg2+pdTM3YwjV9m1sdnghBv6/bxQ0ffYE//iuSPFfy/nVDSEuIqtUYdE1nWPvLGNb+MgC25G3miV/v579nv12rcYjaIQmLEHWIpmmc1iaRJ5w6+EyinFVcb0DUeXklPibPXcNbf36OHreYdtqNvHVDP+KjqncNr6rymT5+3zSbhMgES+MQNUcSFiHqmPnrc2lSWsIKu4v/dG9sdTgiRHj8Bu/9sYkX503DG/0bpt6GM+L+zfOXnkyEw/rEd8WOpXy05lPeGfYxXsOLQ3fgM32syl3FnqId5BXlUFi6h3rRyZzRahgR9girQxZVJAmLEHXMl39uo6U3ki2NI3HZpd+9OLZFm/Zwx2dz2e74BFNrSFrpjdxzbhcGtU8KmsncuiafxK3dbubBH+8AwK8CCyu2j2tJYmQi7uiG1I9rRs7eddw480pePGsK0Y5oi6MWVSEJixB1TGq9KLbrBjGR9qD5sBHBqdRn8Ox3q3h72f+wxawiuvAf3HnGaVzUIxV7EC00uk//lucce+2h1L6kuJvw0vyJ3NX3sdoJTFSL4PuLE0LUqP5tG7DNZpC8cQ93fpSJx29YHZIIQjl5pZz90pe8t/EhFDA08T5+uOUSRvRqEpTJSlWkNzmDlXvWHNe5q3NX8+eOzOoNSFRKaP/VCSGqrEtqHN3P6UihrrD9upkxby60OiQRZPJLfVzx1myynW8RVXARk4fdwrOXdicuymF1aMdlS/4W/vHpUF5Z8DSmMlmxczlpUQ2rdI1t+X9z88wrmbb0LZ75/RF2l+yuoWjFkUiTkBB1jKZpjBvchttziyn5fT0b1uWilJLmIQEEpti/8cN5bHO8RUzhJUy/4TxS69XucOXq1jC6IQmuOOy6gzHfjKJBRAI/5sznHl8xUY5jl81v+rn/p3/xWL9n2FnwN9uWvk68K77mAxcVSA2LEHVUar1I4gwH7uRoSVZEuUe/XMbCwlcg7wzevPzckE9WAJw2J2O63si8nAW0im1C7yYDuLPLWJy2yg3FXr7jL7rV78jO4h0szF5Avq+IW2Zdw4wVHx71PL/px2f4qqMIAqlhEaLOahQXyS4NDMO0OhQRJN75fSMfrX8Vw9OG/553EZ1T46wOqdqc3OR0eqadRtaeLD796012e/bQpmF3OiZ1BaDYV8zSHUtYm7OIQl8RPtOP1/QRY48iyd2EH7PngVIs3r2cd877GL/p5/9+exhWfMh5HS4/5PUyty3g/zKexGVzcXm7yxjQalgtlzj8SMIiRB11UtN4frB78Oyws7fYa/nEX8JaP2Xt5PFfpoDdzh3pV3BWp0ZWh1TtNE2jbUJb7uv/JDmF2byx6DleXPh/aGhE2iPoXK8tbZNPJiYiDofuwGlzsrt4J68tmsTN3W/hhw0z6Vy/AwB23c6dfR/lpplX0rvpQJKi9/eJ8RpesDk4Nekkrj3lLm6dda0kLNVAEhYh6qjWSbFsrRdH+o5SbvpgEU9e1DUsqv9F1a3dUcDN06aiR23i7IZ3MLZfS6tDqnHJMY24v99T+AwfDtuROxO3jG9Jg4hEMjb/wJiT/02au0n5Pl3Tub3XPfx7zk3EudzE2KMp8BWyrXg7DVzxjGh/Bb9tmE2X+h1ro0hhr0p9WCZPnkyXLl1wu9243W7S09OZOXNm+f5p06YxZMgQEhMT0TSNzMzMSl33+eefp23btkRGRpKWlsbtt99OaWlplQoihKgam67x74u7sM7hp/mfuxkzYQ7fLs+xOixhgce+ycTvnkMn59VMuLBznerTdLRkZZ/mCa24pNv1FZKVfdokduDd4Z8yacgb3Hv6EzwzeDJTz/+cf/d5hDNankOsK46dJTtrIvQ6p0o1LKmpqUycOJFWrVoB8M477zB8+HCWLFlCx44dKSoqok+fPlx88cVcd911lbrmBx98wD333MObb77JqaeeSlZWFldeeSUAzz33XNVKUwOUqfjj83XUbxyDMhWmqQL3hkKpsnuT/Y/Ltx1wrFl2zCHbDr5eTRRAwYm8+Zzo+TVM0wLVvGjgLfFbHU7IGdi+Ib5revPGj+vokrWbJ99cyMqzWjNuUJs69aFVl+0u9PBHzlxM1YMnr+2Jy279NPuhSNd0Yp2x5c9b12sNQI/UPry59A0M08Cmy8/2RFQpYRk2rGIb3OOPP87kyZOZN28eHTt2ZOTIkQBs3Lix0tf8448/6NOnD5dfHui01KxZMy677DIWLFhQldBq1M7NBSz5bjNdB6WR0ioe3aah6Rq6vu8eNF1H0zlgW8X7wL7DHGPT0LXAfk3X5EOiCpQqS/KUQgHykzs+Z3VqxMD2Dbn4uV/ouiWXlV+tZG6TepzRNsnq0EQtWL4tHxzbaWw7lZYNYqwOJywNShvAnbPHcEuv8byd+RJ/F23jzLSBxLhiSY5JpUdqH6tDDAnH3YfFMAw++eQTioqKSE9PP+4A+vbty/vvv8+CBQvo1asX69ev55tvvmH06NFHPc/j8eDxeMqf5+fnH3cMR6PpGsNv605pkY9Zry2j70Wta+R1RNVpmlZW+SOpyoly2HQ+uf00bvpgMQ0X5DB7xXZJWOqIPcVedOcekl2yEGZN+UenkfRMO53JGU8TH1GPyUPf4fu1MwB4ecmLvNiwe6Xmg6nrqpywLF26lPT0dEpLS4mJiWH69Ol06NDhuAMYMWIEO3fupG/fviil8Pv9jB07lnvuueeo502YMIFHHnnkuF+3qiKiHTicMm2NCF8Om87ZnRsxL2Mb32dmc+WpzWjTMPbYJ4qQtrfYh6b5qB8ttSs1KS2uKU8M+m/586FtLwKgwJPP16s/5uJOV1oUWeio8idw27ZtyczMZN68eYwdO5bRo0ezYsWK4w5g7ty5PP7447z88sssXryYadOm8dVXX/HYY0dflGr8+PHk5eWV37Zs2XLcMQghAk5tWZ/l0XD2bi+PTJzDDe9mMGuZdMQNZ3uLAxObxUXKsHYrXNBpFL/+/Su3zLqaa766nE/+etPqkIJWlWtYnE5neafbnj17kpGRwaRJk3j11VePK4AHHniAkSNHcu211wLQuXNnioqKuP7667nvvvvQ9cPnVC6XC5fLdVyvedykf4kIc0nuCB6/+XRe+C4L/9KtNFywnbeXbKXBbf3o0bSe1eGJGlDk9QOK2AiZ5cIKdt3Oc0NeA8BUJnd8dwM/bf0Fp83JeS3PO/bq03XICf+FKqUq9CWpquLi4kOSEpvNVtahsiaGzQghjqZLajxvXN2LpX/ncc3bGQzaXsLK7HxJWMJUkccPaEQ5ZQSLVXRNL79/4awpQGDm3Wu/+Se7inexeHsGN/S4nab19s+PU+gtZFXuKhrHNCY5OrlODNioUsJy7733MnToUNLS0igoKGDq1KnMnTuXWbNmAZCbm8vmzZvZtm0bAKtXrwYgOTmZ5ORkAEaNGkXjxo2ZMGECEBh59Oyzz9K9e3dOOeUU1q5dywMPPMB5552HzSb/gYSwSufUOHo2jceR42Hxplz+2bup1SGJGhBIWJCEJchEOaK4rPVFeE0fY3vdxX9+uZd/n/oQWdsXE+WMZcnWP8j15uOyOckp2YWpTE5tdAoju99odeg1pkoJy/bt2xk5ciTZ2dnExcXRpUsXZs2axeDBgwGYMWMGV111VfnxI0aMAOChhx7i4YcfBmDz5s0ValTuv/9+NE3j/vvvZ+vWrTRo0IBhw4bx+OOPn2jZqp/U+Ig65or0Zry7cBu5CzayY2h7ktwRVockqtmu4nyU6ZKlGYLQsAPWKLqs3WXMWPYubep3YPOedewozWVc7/toFJsCwPzNc5me9alVodYKTYVJu0t+fj5xcXHk5eXhdrtr5DW+fulPzrmpa41cW4hgdO3bGSQs3sbvTjtfP3YmcZHHnhVUhJazX/qSdb4ZvDzkPwxs3/DYJ4igdOZH/elUry0+088FrS/gjBDq+1LZz2/pZVUVdaCNUIh9cvJK2bRsOw2VSbtODSRZCVNF3hJQdqJd8nEQyj4871M0NGKdsTz3+6N8ufYLLmx7MX2aDbY6tGojf6FCiMNav6uQbl6Dn1wOZl/ew+pwRA3xKQ9KOXHZZZ6pUJYYmVj++O7T/sOe0j3c+t11J5ywmMoka08WGhpt6lm7ZIf8hVaSMsOi5UyISuvVLIFFkS56e/2cdt+3vPTDGkz5fxB2vIYHTAdOSVjCSr2IeqRENiRz2/wK2w3TYHfJbrILs9lWuA2PcfRRvi/Nm8jnS9/mg8zJfJg5uSZDPiapYakkUyl0XZqERN1ht+l07NyAXYs2crrXw4ovVjA12sXlpxy6Yq0IXT7lQWGXGpYw9EC/J7nvh9tosu5LSgwP20p2oqOR4IrDpbtQKHZ79lBslNIpvg0XdL6KxrGp5ecrpViau4JXz36P5buX8+uaGRaWRhKWSjN8Jja7JCyibnnhipPYPLQdN7y3iB7r8tlT7LU6JFHNfKYPNHu1rNK8ceNGmjdvzpIlS+jWrVuVzv3222956KGHWL58OREREZx++uk888wzNG/e/ITjqqtinDE8e+arrNm7hlhnLCnRKYdt0jGVyV/bF/PUbw/SP/UM/tFpJB7Dw+sLn6NXw55omobX8OLUre3HJil1JZl+hU2+gYg6RtM0mtaP5swODdEU/LVpj9UhiWrk9Zv4zBKU4SLCYd08LOvXr2f48OEMGDCAzMxMvv32W3bt2sUFF1xgWUzhwqbbaJfQjsYxjY/Y/0TXdLol9+S5IW+wIW891339T27/9nqauZtzbc/bAPD4Pbhs1g59l0/gSjL8JrpDflyibkpvmUi+zUf2tkKrQxFHYZomTz75JK1atcLlctGkSZPyOa2WLl3KgAEDiIyMpH79+lx//fVkrNmK5txOpNaAi4cN4bbbbqtwvfPPP58rr7yy/HmzZs144oknuPrqq4mNjaVJkya89tpr5fv31YZ0794dTdPo378/P//8Mw6Hg5ycimtS/etf/+L0008HYPHixRiGwX/+8x9atmzJSSedxL///W/+/PNPfD5fDfykxOHoms4dfR7itbPf4+Wz3+Hc9peW7/P6i3HarZ2HSZqEKsnwm1LDIuosj99AaSbeUr/VoRyXqfM38fnX88E0QYN93zM1TQs81jTKHpXPXrDv26jSNJSuo2w2Turein+ddfyr09e08ePH8/rrr/Pcc8/Rt29fsrOzWbVqFcXFxZx11ln07t2bjIwMduzYwbXXXssfa7KxXWzn9Lh/spyplXqN//u//+Oxxx7j3nvv5dNPP2Xs2LGcfvrptGvXjgULFtCrVy/mzJlDx44dcTqdJCQk0KJFC9577z3uvPNOAPx+P++//z4TJ04EAuvS2Ww23nrrLa688koKCwt57733OPPMM3E4ZDh9bTtcTYzXV4zTHmVBNPtJwlJJkrCIumzZ1jzi/S4aNauZSRlr2hdzMrn22xdZ3qDZYfcfuXeaQleBW1r+dj5ZNQD/4HbYbcH3XlBQUMCkSZP473//y+jRowFo2bIlffv25fXXX6ekpIR3332X6OhoAJ569nkuvuB8mvW5mVFnNefu1yv3OmeffTY33hiY/v3uu+/mueeeY+7cubRr144GDRoAUL9+/fLlWACuueYa3nrrrfKE5euvv6a4uJhLLrkECNTcfPfdd1x88cXccMMNGIZBeno633zzTbX8bMSJ8/iKcDoiLY1BEpZKCiQs0ulW1E2N4iLZohnkbC/C6zdDbgis4fGyMrEp9e57jLYNY1HsX2lDoSj7V2GbKt+myCvx8cvE/8NEwxakowVXrlyJx+Nh4MCBh93XtWvX8mQFIKF5F1CKyNwYejVPqPTrdOnSpfyxpmkkJyezY8eOo55z5ZVXcv/99zNv3jx69+7Nm2++ySWXXFIeT05ODtdeey2jR4/msssuo6CggAcffJCLLrqI2bNn14mF/YKd11tEXNkyAFaRhKWSDJ90uhV116AODXkj1kmbnL1M+XUDY/u3PPZJQcTv82NqOl1S4+mWFl/l87fnl7IAP36bI2g/PCMjj/ztVyl1SNwFpYG+IXERbjRNQ9d1Dl6p5XD9Rw5uotE0DdM0jxpbUlISw4YN46233qJFixZ88803zJ07t3z/Sy+9hNvt5qmnnirf9v7775OWlsb8+fPp3bv3Ua8vap7XKMHpiD72gTVIPoErSZqERF0WF+ng7IGtiFQ6X/68yepwqsz0+TB0HedxNuX4TYXdNDEtHtZ5NK1btyYyMpLvv//+kH0dOnQgMzOToqKi8m0ZC/4ATaNRajsAGjRoQHZ2dvl+wzBYtmxZlWJwOp3l5x7s2muvZerUqbz66qu0bNmSPn36lO8rLi7GZqs4Smnf82MlQ6J2ePweXBZ3upVP4CpYs3A7q/7IPvaBQoShIZ2S2aWb+AqK8Buh9SESWVxAkTPiuJuy/IaJrkyohrlKakpERAR33303d911F++++y7r1q1j3rx5TJkyhSuuuIKIiAhGjx7NsmXL+PHHH3lt4gO4T25CcsM0AAYMGMDXX3/N119/zapVq7jxxhvZu3dvlWJISkoiMjKSWbNmsX37dvLy8sr3DRkyhLi4OP7zn/9w1VVXVTjvnHPOISMjg0cffZQ1a9awePFirrrqKpo2bUr37t0r9drNmjULdKI+6HbTTTcBUFhYyM0330xqaiqRkZG0b9+eyZMPnbn1jz/+YMCAAURHRxMfH0///v0pKSmp0s8hHO327KV+ZH1LY5AmoUpKaR3PsFu68cvHWbRLb2R1OELUup+zdpLo9VDgLeayhz7GbrOh6TpK09BsNtADzQroOmg6mh64oWug29BsWtlzG7oeeKxpOrquoWta4DSt7LEGuqahaWDTKu63aYF+JLqmYdNB1ytuKz9fD5yvoRGXv4NdEXG4I47vLc9vKmymgaHpLNlccS6aA5taWjSIxh1hXS3MAw88gN1u58EHH2Tbtm00atSIMWPGEBUVxbfffsu4ceM4+eSTiYqKol36IHad4SPWFYj36quv5s8//2TUqFHY7XZuv/12zjjjjCq9vt1u54UXXuDRRx/lwQcf5LTTTitv+tF1nSuvvJInnniCUaNGVThvwIABfPjhhzz11FM89dRTREVFkZ6ezqxZs47a1HWgjIyMCjU7y5YtY/DgwVx88cUA3H777fz444+8//775Z18b7zxRlJSUhg+fDgQSFbOOussxo8fz4svvojT6eTPP/8M/F3XcUVGKTGOGEtj0NTBjZYhqrLLU58Iw2cy+83lnHVD5xq5vhDB7PknPic+cwN5xYvRUOgKNBSaKruVPdYPeBzYTmBb2XZdmWgHnAugDjNOR2P/W5NCQ0NVOO7A/QduO9y1Ekrzebn3aKY/d/Vx9UFZu6OAj8b8m0YFu9kTGXvYYzQUv7YbwMcT/hmUo4gONuGblby7/iGuaPYw959bO0O1r7vuOrZv386MGTU/xfttt93GV199xZo1a9A0jU6dOnHppZfywAMPlB/To0cPzj77bB577DEAevfuzeDBg8ufi/3umXMz958+kRhn9Sctlf38lhqWKtBtGoYRFvmdEFV29ZV9mfFQCekTn0cBpgp05jQVGEphKhV4bgbW3jpwv1m+Hwxz/2PzgP0Vjw2cGzi2bJt5wGuZav/9gY/Lr7vvtfeff3OnRsfdYbZZ/Wi2nnUFf23YvH/jQQtBpq3PpPmWvygo9VMv2toZQSsjv2xOndhaqBHKy8sjIyODDz74gC+++KLGX8/r9fL+++9zxx13lP/O+/bty4wZM7j66qtJSUlh7ty5ZGVlMWnSJAB27NjB/PnzueKKKzj11FNZt24d7dq14/HHH6dv3741HnOwK/aXEGmXYc0hQwvS4YxC1AZ3SiJNG/qJ/3MNbf5Rt0Zt2G06L19/+lGPueTaHbTL34DHHxr9e/aNEoo9zmayqhg+fDgLFizghhtuYPDgwTX+ep9//jl79+6tMEvvCy+8wHXXXUdqaip2ux1d13njjTfKk5H169cD8PDDD/PMM8/QrVs33n33XQYOHMiyZcto3bp1jccdzBSBaf6tJAmLEKJSlFIYmg3PngKrQwlKhjMKl+Gl1HfoCJlglF9ailJ6rSQsBw5hrg1Tpkxh6NChpKTsnzfkhRdeYN68ecyYMYOmTZvy888/c+ONN9KoUSMGDRpUPhrphhtuKO8U3L17d77//nvefPNNJkyYUKtlCDZKWZ+IS8IihDimhZNmsH55PkmNI+nwz6p1xKwrVEQELr+PUn9oJCx5pYVgumqlSag2bdq0iTlz5jBt2rTybSUlJdx7771Mnz6dc845BwhMgJeZmckzzzzDoEGDaNQoMJiiQ4eK/Xnat2/P5s2bqet8pv+w8/nUJklYhBBH5C0sYfFL35C3q5QL/jsCu1PeMo7IFYHD8OPxWf9NtDIKvEUo5TrukVPB6q233iIpKak8MYHABHg+n++Q0T42m628ZqVZs2akpKSwevXqCsdkZWUxdOjQmg88yDmDYNLE8PpLFUJUm9LcfD6762uat3Ix8D8XS7JyLBERuAxfyDQJlfj8YIcoV/j8Xk3T5K233mL06NHY7fvL5Xa76devH3feeSeRkZE0bdqUn376iXfffZdnn30WCAxPv/POO3nooYfo2rUr3bp145133mHVqlV8+umnVhVJHCB8/lJrQZiMABfiiEzTpGhjDprTzuz/zKL74BZ0uFRGSFSGFhGJ0/CHTKdbUwW+LduCdKmB4zFnzhw2b97M1Vdffci+qVOnMn78eK644gpyc3Np2rQpjz/+OGPGjCk/5rbbbqO0tJTbb7+d3NxcunbtyuzZs2nZMrSWoghXkrBUgVL7l54XIhyt/GAuf/6UQ6TLpPv57Wl21slWhxQyPDYHTr8vZN4jAt+/VMjEWxlnnnnmEb9YJicn89Zbbx3zGvfccw/33HNPdYcmqoEkLFVhcYcjIWqaKz6GyEid854fgS0EJj8LJl6bHYdphEyNhVKAFl4Jiwhv8o5UBVLDIsJdy3NPpnFjnd8en3bsg0UFyu/H0DT0EJmvSZU1CWmHmRlYiGAkCUtVKJD/2yKcaZpGz7su4u+//Zg+n9XhhBSXp4RSuxN7yCQsAApZJkeECvlTrQoNDrN8iahhEyZMQNM0brvttsPuv+GGG9A0jeeff75W4wpXuq7ToJ5J9rzVxz5YlLN5Syh1uEKshkVJDYsIGZKwVIW271uJqC0ZGRm89tprdOnS5bD7P//8c+bPn19hRktx4rwlPqIaJ1odRmgxFIamh0wfFkMp0BQhkl8JC6mytb6sJglLFWjI0ObaVFhYyBVXXMHrr79OvXr1Dtm/detWbr75Zj744AMcjvCardNqickRbP55hdVhhBbDj6np2II8A8gr8fHQF0spcf2OWdqYmDCbOE5UP0073NrotU8SlqoIkW9O4eKmm27inHPOYdCgQYfsM02TkSNHcuedd9KxY0cLogtvXcecxarfs9k8Z7HVoYQOw8DQgzdhUUrxUcZm+r34Jh///TDKjGB0xxE0irN2BV4RGvQg+PyT1LoKNGkSqjVTp05l8eLFZGRkHHb/k08+id1u59Zbb63lyOqGiIQ4hj16FrMemUnW7JUMevIKq0MKfoaB0rSgTVg+Wfg3D/z8f2iOUlI8V/PoBb04rXUDq8MSotKkhqUKZA6W2rFlyxbGjRvH+++/T0RExCH7Fy1axKRJk3j77bfld1KDopLrc8Hkf4LNxsxb36ZkV57VIQU3w8DQ9KD4Jno48zfswpX4A4OSbuC7cUMlWREhRxIWEXQWLVrEjh076NGjB3a7Hbvdzk8//cQLL7yA3W5n7ty57NixgyZNmpTv37RpE//6179o1qyZ1eGHnUFPjMAVH0X2HyutDiWoaYaJqelBO6zZb4K/oD3dm8TjkEkBRQiSJiERdAYOHMjSpUsrbLvqqqto164dd999N40aNWLIkCEV9g8ZMoSRI0dy1VVX1WaodUa020lpfonVYQQ1ZQZqWIK1SWhfXIYp7dqiaoJlsIkkLCLoxMbG0qlTpwrboqOjqV+/fvn2+vXrV9jvcDhITk6mbdu2tRZnXRIVH0FxbrHVYQQ1rawPS7DOw7Kv5scvCYuoIr/yY9NsVochTUJCiGNrOqgb65dJH5aj0U1fYJRQkPZhsZVNaSs1LKKqfIYPp2791BFSwyJCwty5c4+6f+PGjbUSR13lbpZMTKRJzoJVJPdqZ3U4QUk3/fiwBXGTUOBeEhZRVR7DExQJi9SwCCEqpfO5HVg2fYnVYQQl01TYTQO/HrwJi13XASUJi6gyr+GVhEUIEToa9+/K7t1G0HTACyaGUjiUH0OzBXGTkAZo0odFVJnXDMGEZfLkyXTp0gW3243b7SY9PZ2ZM2eW7582bRpDhgwhMTERTdPIzMw85jX79++PpmmH3M4555wqF0YIUXM8ewvxKxumz291KEHHMBUO08Cv2YJ29WN7+Sgh0+JIRKjxGT6cNusTlir1YUlNTWXixIm0atUKgHfeeYfhw4ezZMkSOnbsSFFREX369OHiiy/muuuuq9Q1p02bhtfrLX++e/duunbtysUXX1yV0IQQNWRv1mayPp/P+iwPfS9uh81p/RtXsDGVwq78+DRbWdNL8LHbAqsze/ySsIiqCZY+LFVKWIYNG1bh+eOPP87kyZOZN28eHTt2ZOTIkUDVOkAmJCRUeD516lSioqIkYREiCKz/egELv1hD+74pnH/DKUTERVkdUlDymwq7MvBr9qCtYWmaEI1CZ1XOHqtDESEmJJuEDmQYBlOnTqWoqIj09PRqC2jKlCmMGDGC6OjoarumEOL4LPlyNec8MIjOo86QZOUozLKExacHbx+W7k3i8ed34a+ij/l97S6rwxEhxGf4cOjWDyqucsKydOlSYmJicLlcjBkzhunTp9OhQ4dqCWbBggUsW7aMa6+99pjHejwe8vPzK9yEENVnx8LVRETZiE5raHUoQc8wA51u/diDdpRQ64axnNfqHJTmYez0d9giEwGKSgrZGpa2bduSmZnJvHnzGDt2LKNHj2bFihXVEsyUKVPo1KkTvXr1OuaxEyZMIC4urvyWlpZWLTEIIQJKduURHWv9m1QoCCQsBoZuC+oFOZ/4R2faOC7HG/U7V77/NUUe6UAtjs3nK8Vhc1kdRtUnjnM6neWdbnv27ElGRgaTJk3i1VdfPaFAiouLmTp1Ko8++miljh8/fjx33HFH+fP8/HxJWoQ4wOx7PiRvb8UOlhqg6aBroOuBW5GKwqscNIwswGbTsNlBt2l4iv3k7g3SDhlBxlRgUwZGEFSbH02Ew8ZrI0/h3Jd3sS3ufW7/OIVXrjg1aJcTEMHB5y/BYY+wOowTn+lWKYXH4znhQD7++GM8Hg///Oc/K3W8y+XC5bI+4xMiWOXtNbnolYr/n0yfH8Prwyj1YXq9GF4/3iIvvlIfumng9/jKjvFjeA06N4yzKPrQoymFoVu/3sqxNIqL5LXLB3DZezn87H+FF39owLhBbawOSwQxr78YpyPS6jCqlrDce++9DB06lLS0NAoKCpg6dSpz585l1qxZAOTm5rJ582a2bdsGwOrVqwFITk4mOTkZgFGjRtG4cWMmTJhQ4dpTpkzh/PPPP2RROyFE9dEddnSHHUe09W8+4camTMwQqajo0TSBx4acx/0//s1/F79J+0a3cWbHZKvDEkHK6yshOqKe1WFUrQ/L9u3bGTlyJG3btmXgwIHMnz+fWbNmMXjwYABmzJhB9+7dyyd9GzFiBN27d+eVV14pv8bmzZvJzs6ucN2srCx+/fVXrrnmmhMtjxBC1DpNA91UGEGwom1lXXpyEy5rcwW2iK3c/sU0srYXWB2SCFJefwlOh/WjBKtUwzJlypSj7r/yyiu58sorj3rM4Raxa9OmjUz3LUQ1C5Ev+2FB7evDooVWn58HhnVk5ZSrWWafxDXvJ/PV2GHERUlHa1GRz1+Kw259rWxo/e8SQogg5DNMbEqh2UPrw95h03n1ij7EF1/Groj3ueWj+bI4ojiE1/ThslvfZ1QSFiGEOEEev4GmTGyO0GkS2ich2snrl52FyuvHgvw3eOH7LKtDEkHGa/px6k6rwzjxUUJCiOAk35Nrj8dvYlMmxX64//Ol2DQNm65jt2nYdK3suYZd19DL7m0H3Nt0ff82m4bTpuOw6TjsOg6bhste9twWOM5QCsMM3EwVWMtIqcCozYRoJy0axFQp/k6N43jirAu558d1vJYxi6v6NCc+yvoPKBEcPKYXp836vwdJWIQQ4gRFOe1siUrimmXTMZd/jkIjsNTgfg7TT4cdm/gzueUh52soDE3Hp2mYmobSdAxNx9Q0TE3Hr9v232yBe4WGrhQ2ZaApVfbYZEFSO2657wZ6t6jaiMt/dG/Mf3/pw1btJ+au3sn53Ruf2A9FhA2f6cMRaqs1CyGEOFTzxGiGjh/Hup2F+M39tR+GqSo8X2gqDNPEbyrMA/cZJqZhYvj95Te/38T0+jD8fpTPh/L6UD4fmt9LhDKwaYBuQ+k2lE1H2WzsyC3i6qyvyc4bVeUyaJpGepN2TNv6KSuz8y1NWNb99Qs7r7uR3NRY9mV+yqah7Lb9N4cN7DZw2MtuNrDb0R1OdKcTm8OBLSISW0Q09ogo7BEx2CNicERE4oyIwu6KxBERhSMyGmdENM7IGJyR0TidEdg1e1DPWFzbvKYPVyjOdCuEEOJQQzs3sjoERr7wPb51dlz24+tL0yElnmlbYXn23uoNrIryczazd+hJnP3wO+Xb/F4PPm8J/tKS8nu/txS/pxTDU4Lf68HwBJ77PCX4S4sxSovxlRZi5O2gxLMJ01OK6fVi+gLJn/L5wetD8/nRvH40n4EyzcOOWj04fTHsGoZdx7BrmHYbymlHczgCiZPDge5wgNOB7nSgOZzYnE5sLhc2VxR6RCS2yCjskbHYI2P2J08RkTgionFEROF0ReGyu3DoDhy6A6fNid2imZS9hk/6sAghhKg+pseDT7fjsh/feIr2jdyY3iRWFK0F0qs3uCrwlRSjOys2QdidLuxOF8TEWxPUAZRS4PNhejyBn3lpCV5PEb6SInylxYGbpxh/WQJllBZjeDz4PcUYe4oxS3egPKWYnlI8Hg+lZTVo+PzgK7v3G4G+SZiYSmGiAq97cFsjoDQw7Xp5ArUvmVJOO9gDNVCa04HmCCRQOBzozkBNlN3pRI+IxOGKxh4ZgysmnpjYJGJj6hEXk0hcvWT27t5OZBAMa5aERQghwoRZ6sFvsx13DUu75FiM0jTytfXsLPDQINaaZgCjtAjdaf3aNUeiaRo4y2pNYmNxAFZOq6ZME+X17r95PJgeLz5PMV5PMf6SYnyekvJEyigtxfCW4i8txV9Ugrm7BMOTE0isSovZXVrMdo8Hv7cUiksZubmYyH9Y//uQhEUIIcKE2lfD4ji+GpYop52UiDZs12eyKiefBrENqjnCyvGXFKJHWj+zaqjQdB0tIgIiKiYV1ZVubhl7Izab9emCzMMihBBhQnm9+GzH3yQE0DGpCbp9Lyuz86sxsqoxSouxuyRhERVJwiKEEOHC68F3Ak1CAK2SYgFYt6OouqKqMqOkGFtktGWvL4KTJCxCCBEmlMeLX7cddw1Lqc9gTtZ6AJLjrOuzYHpLsUdWbfI7UYOCZK0/SViEECJMaF5PoEnoOPqw+AyTGz74jY3a69jzz+aKU5rUQISVY3pKsUdIk5CoyPpeNEIIIU6YaSqc/hJKNFeVm4RMU3HHxxksKPwvau9g3vzncJLcVtaw+LBHWD+Mti4w8vPZct312OrVgyNMlqe83lqO6vAkYRFCiDDgNUyiDA8lNleVmoSUUjz85VJm75qEmZfOKxddwMnNEmow0krE5PXhkBqWWmEWFBDRqRPJD9x/xGMON5GeFSRhEUKIMODxmUSZpeTZYsjOKyU2wk6Ew0akw4bDph1xqvnn56zm443P4i/szDPnXMKAdg1rOfJDKa8Pp3S6rRWmx4vmPPostsGyTIEkLEKEIWWaHDqZuAhnXsPEafrot3MJU8f+C5/NTonuolh3UWqPwO+MAlcEmtOF5nJhi3CxvdRkl/N3YvRExp12HsM7NkApZfkHlPL5cMiw5lqh2fSgafI5FklYhAhHfv8R26NFeEqMcVIy+EK+WroK5fViekqJ8pcSZXqI9hcT6duDM9+Pw/TjMPw4TINOhh/NuQW304d7zXy+fs1ENw6o/j+wJeDAKeEPemzqGqZNB7tevjghNh1lDyxKqNlsgXt7YKp4zWFHtzsCCxS6IrA7o7BHRmN3xWCPiELfvkdqWGqJkV8Q6L8SAiRhESIMmX4/mowBrFM0TeOFa/oCfYFAvwOfoSjxGXh8BiX7bl6DUp9JadlzXdMY2D4Jh+34/mCUUuD3Y3g9+MoWI/R7S/D7PIF1dLwe/N7ANO+G14Pp85YvWhhYsLAYf2kRRuEuSj1/Y3hLsHVpS/3kFtX40xFHYpYUo0eGRgdnSViECEOm35AaljpO0zScdg2nXYdIx7FPOIHXweHA7nBgj5a5U0KN8njQIqxZM6qq5DuYEGFIeX3okq8IIY7BLC1Fj7B+YcPKkIRFiDBk+g2pYBFCHJMq9aC5JGERQlgkkLBIxiKEODrlKUWXJiEhhFWU348mbUJCiGMwpYZFCGEl0ydNQkKIY1OeUjTX0SeOCxYySkiIMKR8MqxZCAEFc+eCaaI5HGgOJ5rTGXjsdKI5Hfh37AyZTreSsAgRhky/gS5NQkLUacrnY9eL/yV+xKUorxfl86G8vgMee1GGgSM11epQK0USFiHCkOmTeViEqOuMggJcbdpQ7+KLrQ6lWkilsRBhSDrdCiH8O3Zgb5hkdRjVRhIWIcKQNAkJIYw9e7AnJFgdRrWRhKUKlFLHPkiIIGD4DGw2q6MQQljJyMvDFhdndRjVRvqwVJUkLSIE+L2SsAgR7oy8PLybN5eN/HGWj/zRnYHH/txcHCkpVodZbSRhqQJN0/CWGvzw3ko0XUPTNHSN8seaXvZY19DKtusHPNa0isdV2HfwcXrg9fSD9x147QOOO9zrokF0nIuI6Jpb+EwEJ5/HwGaXJiEhwtnez6bhWbsWe4MGgRE/+277RgD5vCTdeafVYVYbSViq6Nybu+LzGCilUKZCKVCmwjQrPg/sDzQjmaYCxaHHHHz+Aefs22b4zCOff9BrHO7a2zfm0/PsZqS2rQeA4Tex2aUlMNwZPj92myQsQoQz5fVQ79JLiOza1epQaoUkLFXkcNlwuEKnrt1b6uf3z9ayeNZGfB4Tb6mfliclkb+rhBbdGtCiWwOrQxTVYOr1/8OuG+XP/YZGx97yuw11P//8M08//TSLFi0iOzub6dOnc/7555fvLyws5J577uHzzz9n9+7dNGvWjFtvvZWxY8daF7SoNabHg+YKjXWAqoMkLGHOGWGn/xXtyp/7PAZbs/bQ8bQUvpm8lKad6kuNSxiw6wYXvfJPq8MQ1ayoqIiuXbty1VVXceGFFx6y//bbb+fHH3/k/fffp1mzZnz33XfceOONpKSkMHz4cAsiFrVJebxoTklYRJhyuGw065wIQJuTG7Jx6S5adg+fcfpChJOhQ4cydOjQI+7/448/GD16NP379wfg+uuv59VXX2XhwoWSsNQByuNBD5F1gKqDfLWuwxIaR7N7a5HVYQghjlPfvn2ZMWMGW7duRSnFjz/+SFZWFkOGDLE6NFELlNeD5qw7CYvUsNRhqW3qseS7zRTklhKbEBqLXwkh9nvhhRe47rrrSE1NxW63o+s6b7zxBn379rU6NFENin7/nb2fflY2XPnQhQtLli9HC5GFC6uDJCx1mKZr9L2oNRlfb2DAyPZWhyOEqKIXXniBefPmMWPGDJo2bcrPP//MjTfeSKNGjRg0aJDV4YkTVPLXX8RfcjGutm0PWrgwMHw55rTTsMXGWh1mralSk9DkyZPp0qULbrcbt9tNeno6M2fOLN8/bdo0hgwZQmJiIpqmkZmZWanr7t27l5tuuolGjRoRERFB+/bt+eabb6pUEHF83IkR5O8swVvqtzoUIUQVlJSUcO+99/Lss88ybNgwunTpws0338yll17KM888Y3V4ohqYpaXY4uOx16uHIykJZ2pjXC2aE9G2LZGdOxPZpYvVIdaqKiUsqampTJw4kYULF7Jw4UIGDBjA8OHDWb58ORDo0d6nTx8mTpxY6Wt6vV4GDx7Mxo0b+fTTT1m9ejWvv/46jRs3rlpJxHGxO210PiOV799ZyZ6cInZsyrc6JCFEJfh8Pnw+H7pe8W3cZrNhmqZFUYnqpDzeOjVs+Viq1CQ0bNiwCs8ff/xxJk+ezLx58+jYsSMjR44EYOPGjZW+5ptvvklubi6///47DkdgRtamTZtWJSxxglp2T6Jwj4dPJy4kpXU8pgknn9OM5BbhswaFEKGosLCQtWvXlj/fsGEDmZmZJCQk0KRJE/r168edd95JZGQkTZs25aeffuLdd9/l2WeftTBqUV2UpxS9DvVROZbj7sNiGAaffPIJRUVFpKenH3cAM2bMID09nZtuuokvvviCBg0acPnll3P33XdjO8piKB6PB4/HU/48P19qBk5E536NiU2IILVdPZSC795YxrBbuh1yXOEeD0V5Hgp2lxLfMJLE1LrTfipEbVu4cCFnnHFG+fM77rgDgNGjR/P2228zdepUxo8fzxVXXEFubi5Nmzbl8ccfZ8yYMVaFLKqR6fHUqU61x1LlhGXp0qWkp6dTWlpKTEwM06dPp0OHDscdwPr16/nhhx+44oor+Oabb1izZg033XQTfr+fBx988IjnTZgwgUceeeS4X1dUpNv0CrPeNmoZx5y3VtD34tZExOxfi+i7KctIbVuP2PqR/D5tHR36pNCqh8zjIkRN6N+//1FXiU9OTuatt96qxYhEbVKlHvQ6NGz5WKqcsLRt25bMzEz27t3LZ599xujRo/npp5+OO2kxTZOkpCRee+01bDYbPXr0YNu2bTz99NNHTVjGjx9f/m0DAjUsaWlpxxWDOFTPs5uzY1M+37+7EsNnYHfaSEyLJS4xkl7DWgCQ2q4e37+9gkat4oiOq1o7q1KKkgIfht/E8JuYfoVpmhh+hek3MQ2FYZiBBSBtGrpNx2Yve6zrgXu7hq/UoHCvh6J9tzwvxXkelBl4k1eAK8pOvYZRxDeMpl5yFHFJkdgdR669MwyT4jwvhXs8+Er96Pay17Np2GyBx7bybQfEZdex2bTyBSyFEOJEKG/dmnr/WKqcsDidTlq1agVAz549ycjIYNKkSbz66qvHFUCjRo1wOBwVmn/at29PTk4OXq8X5xGyS5fLhUt+kTUqqambc27sglIKX6nBtjV76TFkf/+i2IQITr2wFbOnLKfNKcnY7DptT0mu1LVzs4uY89YKGrWIK08IbAcmBmWPlQmmEUhmTEMFkhtDld8cLhsx9VxEx7monxJDdLyTKLcT3ba/I2JpkY+924vZk1NM1oLt5O0sxu8LJEOx9SMw/SbF+V5MU6ERqG2KinMSU8+Fw2XHNPa9pomx77X9FR/vS7ACideRvxFHRNmJinMRFeckuuze4bShlyU6uq6V/ww0PZAgabYDtpetyi2ECH/KMNHsMvvIPif8k1BKVehLUlV9+vThww8/xDTN8t7uWVlZNGrU6IjJiqhdmqbhjLTTrEviIfuSmrrpd3lbdmzMZ+vqPUTHu8pXhj4aw2fSvGsDep3bvCZCriAi2kFyi7hDOhGbhklBrgebXSfS7cBmq9mJn5VSeIr8FOV5KM7zUpTvIWddHn6vgWmWJT+mQpXdV3humOXbDm4h8O/eTazaU6OxCyEscJTmwLqoSgnLvffey9ChQ0lLS6OgoICpU6cyd+5cZs2aBUBubi6bN29m27ZtAKxevRoItLMmJwe+eY8aNYrGjRszYcIEAMaOHcuLL77IuHHjuOWWW1izZg1PPPEEt956a7UVUtSsesnR1EuOZu+OEvZkF1UqYTENhW6ztqZAt+nENYistdfTNI2IGAcRMQ7qV+Oo/bwvv4STWlTfBYUQIghV6Svl9u3bGTlyJG3btmXgwIHMnz+fWbNmMXjwYCAw4qd79+6cc845AIwYMYLu3bvzyiuvlF9j8+bNZGdnlz9PS0vju+++IyMjgy5dunDrrbcybtw47rnnnuoon6hFbXsnszWrct/0TUOhS9NG9TBNtKOMqBNChCjpC1dBlWpYpkyZctT9V155JVdeeeVRj5k7d+4h29LT05k3b15VQhFByBVlZ+/2kkoda5rW17CEC2WYaJqsYypE2JEmoQrkXU5UG9NQlBR4y0foHP1YUxKW6mIaaDXc/0YIIawm3Y9Ftfh7VS4LZ27kvHHdKjWKJdCHRT5kq4MyTdClSUgIEd4kYREnLGd9HvO+WM+5N3clItpx7BOQPizVyjRBfpZCiDAnX3HFCSvILaXjaSmVTlYAlKlkPpFqogxDOt0KIcKeJCzihBXu8RAdX7VJ/KTTbTUyFejyX1mIcKIMQ/5fH0R+GuKEOVw2CnaXVukcZUqTULUxDTR5YxMirCivF90lk6ceSN7lxAnr0DeFNQt3VOkcaRKqPsqQTrdChBvl8aA5ZfmZA0nCIk6YrmvYHTq524qOepy3xF9+jDQJVSNlyrBmIcKM6fGgRUjCciAZJSSqxRn/bMd3U5Zz+og21G8cU2FfbnYRWfNz2Jq1F3diBIbfJLlFHBExUt1ZHfSYWHKeeAJHw7KFJzUNNA3N4UCz2yveOxxoDjuUb3OiORzokRHEX3KJNC0JESSUx4MuC/xWIAmLqBbR8S7Our4T301ZTteBaTTtVB9N0zAMk+/fWclpl7bmlOEt0DSN9Zk7mfP2Cs4e28XqsMNCvUsvod6ll1TYpkwT5fejvD6Uzwt+P8rnC9wOfOwLPN710kvEnXceWlSURaUQQhzILC2VJqGDSMIiqk1krJOhN3Tmzx+28NePf1MvOYqdmwvoNiiN5Ob7V0pu0a0B1z/fz8JIw5+m62hOJzidQPQxj98z9X8yIkGIIKI8XmkSOogkLKJaOSPtnHxOc7ylfgpzPcQ1iMTmkA/CoCdDo4UIKsUZGbhatbY6jKAi71CiRjgj7CSkREuyEipkaLQQQcMsKqLo11+JHTzI6lCCirxDCSECC1ZKwiJEUNjzySfEX3qpfIk4iPw0hBCB9Yg0GWYuhNWU10vhTz8RO2ig1aEEHenDIoQAXWfHxIlokZHokVHokZHoUZH7n0dFoUdFokdGokUe8DgiAk0SHSGqTeGvvxHbv7+sD3YYkrAIIUh5ciLG7t2YJSWYxSWYJcWokhLMkhL8O3fuf15cgllcHDiupBhV6tl/kQMTFxVoYtIjIgLJTVRUhWTIFh9H7JlnokdE1H5hhQhiBd99R4PbxlkdRlCShEUIgS0mBltMzLEPrAJlGJglpaiSfQlOINlRJSX4srPZcsMY6l93HTF9+1Tr6woRqoy8PIy8PBzJyVaHEpQkYRFC1AjNZsMWEw0xh58Hxn3WWex86WXypk+nwe234UxNreUIhQguue+8S70rrrA6jKAlnW6FEJbQo6NpeNedJN50Ezueeprc9z+wOiQhLGPk51OydCnRfU61OpSgJQmLEMJSrhbNSX1hEsUZGZQsW251OEJYIvfd90gYNVI6sR+FJCxCiKDQ6JGHyX3zTbbdM57S1autDkeIWuPPzaUkM5Povn2tDiWoScIihAgKtvh4Gj/7fyTeOJadL75IwQ8/WB2SEDXOLC1l2113k3TXnVK7cgySsAghgoqzSRNSn32W3PfewygssjocIWpUzn/+Q/1rriaiTRurQwl6krAIIYKO5nTiHjKEot9/szoUIWqMf9cuVHEJ0enpVocSEiRhEUIEpZgBA9g7darUsoiwlfflV7iHnWt1GCFDEhYhRFByJCURd+GF5L71FkZhodXhCFGtlFIU/fILMdLRttIkYRFCBC33kCHoUVFsu+tudjz3PMo0rQ5JiGqR+/Y7xAwYgOZwWB1KyJCERQgRtDS7nfrXXE3ayy/hycrCLCiwOiQhTljel1/iWbuGeldcbnUoIUWm5hdChATl96NX83pHQtS23HfewbNuPY0efVSGMVeR1LAIIUJC7OBB7PngQ6vDEOK4KNNk+8QnMQoLSX7kYTSbzeqQQo4kLEKIkBB/0UUUzJ5tdRhCHJcd//d/uFq3osFNN0nNynGShEUIERI0XccWH4d/zx6rQxGiyjyrs4i/8EKrwwhpkrAIIUJGwqhR5L7zjtVhCFEl/p07sSclWR1GyJOERQgRMlxt2uD7e6vVYQhRJaWrs4hoK1PvnyhJWIQQIUN3u0EpCr7/3upQhKg0s6gIPdZtdRghTxIWIUTI0DSNRk88TtH8+Wy+9jr2fPSx1SEJcVTK56Poj99xpDSyOpSQJ/OwCCFCiu5ykXzvvSil2P7Yf8j/5hvcZ59tdVhCVODZsIG8zz6jdMVK4s4fTnTv3laHFPIkYRFChCRN02h473i23DCGqN69sSckWB2SEHg3bmT7M89gr5dA/MUX0eBf/5JhzNVEEhYhRMjS7HZiBw+ieOFC3GeeaXU4oo4rmjeP3a+/QaPHHsWRkmJ1OGGnSn1YJk+eTJcuXXC73bjdbtLT05k5c2b5/mnTpjFkyBASExPRNI3MzMxjXvPtt99G07RDbqWlpVUujBCiDrLZZNZQYbmCH35gz9SPSP3vi5Ks1JAqJSypqalMnDiRhQsXsnDhQgYMGMDw4cNZvnw5AEVFRfTp04eJEydWKQi32012dnaFW0RERJWuIYSom3SXC7OoyOowRB2mDIPc996j8dNPoUdGWh1O2KpSk9CwYcMqPH/88ceZPHky8+bNo2PHjowcORKAjRs3VikITdNITk6u0jlCCAFgS6hPyZ+ZVoch6rDCn34i5rTT0RwOq0MJa8c9rNkwDKZOnUpRURHp6eknFERhYSFNmzYlNTWVc889lyVLlhzzHI/HQ35+foWbEKLuiT6lF8V/zMP0eq0ORdRRez+bRvwF/7A6jLBX5YRl6dKlxMTE4HK5GDNmDNOnT6dDhw7HHUC7du14++23mTFjBv/73/+IiIigT58+rFmz5qjnTZgwgbi4uPJbWlracccghAhdmsOBPakBxs6dVoci6iDPunXY69fHFh9vdShhT1NKqaqc4PV62bx5M3v37uWzzz7jjTfe4KeffqqQtGzcuJHmzZuzZMkSunXrVqWATNPkpJNO4vTTT+eFF1444nEejwePx1P+PD8/n7S0NPLy8nC7ZUZBIeqS/FmzMPbmUW/EpVaHIuqY7IceJuGfV+Bq3drqUEJWfn4+cXFxx/z8rnINi9PppFWrVvTs2ZMJEybQtWtXJk2adELBVghI1zn55JOPWcPicrnKRyvtuwkh6qaonj0pqcSoRCGqkz83F//uXZKs1JITnppfKVWhpqM6rpeZmUmjRjKNsRCicpRhWB2CqIN2v/Y6Cf/8p9Vh1BlVGiV07733MnToUNLS0igoKGDq1KnMnTuXWbNmAZCbm8vmzZvZtm0bAKtXrwYgOTm5fBTQqFGjaNy4MRMmTADgkUceoXfv3rRu3Zr8/HxeeOEFMjMzeemll6qtkEKI8Kd8PqtDEHVISWYmRn6+TLlfi6qUsGzfvp2RI0eSnZ1NXFwcXbp0YdasWQwePBiAGTNmcNVVV5UfP2LECAAeeughHn74YQA2b96Mru+v2Nm7dy/XX389OTk5xMXF0b17d37++Wd69ep1omUTQtQRjoYNsSUkUPjrb8T07WN1OCLM+XNz2fHsc6S+UH3dIcSxVbnTbbCqbKcdIUR48qxfz66XJ5Py9FOydouoMWZJCX+PG0fSv/5FRNu2VocTFir7+S1rCQkhwoKrRQuiepzElmuvI/7ii4kdcmZ54uLbupXS1Vn4/t6CkZePLc5NVK9eOJs1Q5dZtUUlmR4PW/99J/WvvlqSFQtIwiKECBv1LrsM97nnsv3JJ0GZRHTugmfVSvb8byqxgwfhbNkSW1w8/h07yP/6GzxZWTR+YRK6y2V16CLIKb+fbXffQ70RI6TfikWkSUgIEXaMggJ2vfQyyufDllCPhNFXYouJPuS4vK++puDbb0HXqX/ttUR06ijNSeKwdr/1Nja3m/gLL7A6lLBT2c9vSViEEHWeZ+1a8r6YQcnSpdQbMQL3WUOsDkkECbO4GH9uLjkPPUzaa6/KyuA1QPqwCCFEJblatSLpX3dgejzseOppdr32KpEdO5EwaiS2xEQ0hxNN18BmC9TA6Pr+xyJs7f3sM/K//gZnixa4zx4qyYrFJGERQogyustF8gP348vJoWjePPK++AL/7lyU1wvKRJkKTBNlGmAqqK4Kak078rUO3HfwYwg81zQ0mw6aDrqOpgfu0TU03bb/saajOezoUVFoUVHoUVHo0dHoERFodjvY7Wh2O5rdgebY99gOBz3X7HZwONAcjkAy53QEtusnPBdp0CiYO5fixYtJe+P1sCpXKJOERQghDuJITib+/POtDqPSlAokUhjG/semiTroHtNE+f2YxcWBW1FR4L6kBOX1ooqLUT4/yu9H+X3gL3t88LZ9z32+CrfDJl1Hq4VSKpDoOAIJ0f4k6KCb04nucqG5ItAjAvdahCuQaLn23e/bt/845fGgRUai6TpKKXx//03pypWUrlyJZ80a8BuB+A5MBG06yufDnlCf5EcelmQliEjCIoQQIU7TAs1V2GyEUiOVUiqQZB2U+FS4eX0onxfl8WCWlpbdl6D27kV5SjFLPfvvS0sxPaWoUg+mpxTN7gjUjpkmKIWjcWMiOrQnduAgEseMOWR0mFIK/H40h8Oin4g4GklYhBBCWELTtPJmKCIjrQ4nEI8kK0FL6rqEEEIIEfQkYRFCCCFE0JOERQghhBBBTxIWIYQQQgQ9SViEEEIIEfQkYRFCCCFE0JOERQghhBBBTxIWIYQQQgQ9SViEEEIIEfQkYRFCCCFE0JOERQghhBBBTxIWIYQQQgQ9SViEEEIIEfQkYRFCCCFE0LNbHUB1UUoBkJ+fb3EkQgghhKisfZ/b+z7HjyRsEpaCggIA0tLSLI5ECCGEEFVVUFBAXFzcEfdr6lgpTYgwTZNt27YRGxuLpmnVfv38/HzS0tLYsmULbre72q9vNSlf6ArnsoGUL9RJ+UJbbZRPKUVBQQEpKSno+pF7qoRNDYuu66Smptb467jd7rD8o9xHyhe6wrlsIOULdVK+0FbT5Ttazco+0ulWCCGEEEFPEhYhhBBCBD1JWCrJ5XLx0EMP4XK5rA6lRkj5Qlc4lw2kfKFOyhfagql8YdPpVgghhBDhS2pYhBBCCBH0JGERQgghRNCThEUIIYQQQU8SFiGEEEIEPUlYymRlZTF8+HASExNxu9306dOHH3/8sXz/7t27Oeuss0hJScHlcpGWlsbNN998zLWLcnJyGDlyJMnJyURHR3PSSSfx6aef1nRxDlET5du4cSOaph329sknn9RGscrV1O8P4I8//mDAgAFER0cTHx9P//79KSkpqcniHKKmyte/f/9DfncjRoyo6eJUUJO/OwjMojl06FA0TePzzz+voVIcWU2V74YbbqBly5ZERkbSoEEDhg8fzqpVq2q6OIeoifLl5uZyyy230LZtW6KiomjSpAm33noreXl5tVGkCmrq9/faa6/Rv39/3G43mqaxd+/eGi7J4dVU+TweD7fccguJiYlER0dz3nnn8ffff59YsEoopZRq1aqVOvvss9Wff/6psrKy1I033qiioqJUdna2Ukqp3Nxc9fLLL6uMjAy1ceNGNWfOHNW2bVt12WWXHfW6gwYNUieffLKaP3++WrdunXrssceUrutq8eLFtVGscjVRPr/fr7KzsyvcHnnkERUdHa0KCgpqq2hKqZr7/f3+++/K7XarCRMmqGXLlqmsrCz1ySefqNLS0tooVrmaKl+/fv3UddddV+F3uHfv3tooUrmaKts+zz77rBo6dKgC1PTp02uwJIdXU+V79dVX1U8//aQ2bNigFi1apIYNG6bS0tKU3++vjWKVq4nyLV26VF1wwQVqxowZau3ater7779XrVu3VhdeeGFtFatcTf3+nnvuOTVhwgQ1YcIEBag9e/bUQmkOVVPlGzNmjGrcuLGaPXu2Wrx4sTrjjDNU165dT+jvUxIWpdTOnTsVoH7++efybfn5+QpQc+bMOeJ5kyZNUqmpqUe9dnR0tHr33XcrbEtISFBvvPHGiQVdBTVZvoN169ZNXX311ccd6/GoyfKdcsop6v7776+2WI9HTZavX79+aty4cdUVapXV9N9mZmamSk1NVdnZ2ZYkLLX5f+/PP/9UgFq7du1xx1tVtVm+jz/+WDmdTuXz+Y473qqqjfL9+OOPliUsNVW+vXv3KofDoaZOnVq+bevWrUrXdTVr1qzjjleahID69evTvn173n33XYqKivD7/bz66qs0bNiQHj16HPacbdu2MW3aNPr163fUa/ft25ePPvqI3NxcTNNk6tSpeDwe+vfvXwMlObyaLN+BFi1aRGZmJtdcc011hV4pNVW+HTt2MH/+fJKSkjj11FNp2LAh/fr149dff62pohxWTf/+PvjgAxITE+nYsSP//ve/y1c+rw01Wbbi4mIuu+wy/vvf/5KcnFwT4R9Tbf3fKyoq4q233qJ58+a1umJ9bZUPIC8vD7fbjd1ee0vg1Wb5rFBT5Vu0aBE+n48zzzyzfFtKSgqdOnXi999/P/6AjzvVCTN///236tGjh9I0TdlsNpWSkqKWLFlyyHEjRoxQkZGRClDDhg1TJSUlR73u3r171ZAhQxSg7Ha7crvd6rvvvquhUhxZTZXvQGPHjlXt27evxqgrrybK98cffyhAJSQkqDfffFMtXrxY3XbbbcrpdKqsrKwaLM2haur399prr6nZs2erpUuXqv/973+qWbNmatCgQTVUisOrqbJdf/316pprril/jkVNQjX5f++ll15S0dHRClDt2rWr1dqVfWrjvWXXrl2qSZMm6r777qvGyCunpstnZQ2LUjVTvg8++EA5nc5Dtg8ePFhdf/31xx1rWCcsDz30kAKOesvIyFCmaarzzjtPDR06VP36669q0aJFauzYsapx48Zq27ZtFa6ZnZ2tVq5cqT7//HPVoUMHNXbs2KPGcPPNN6tevXqpOXPmqMzMTPXwww+ruLg49ddff4VF+fYpLi5WcXFx6plnnjnhcgVL+X777TcFqPHjx1fY3rlzZ3XPPfeEfPkOZ+HChQpQixYtCumyffHFF6pVq1YV+lJVZ8Jidfn22bt3r8rKylI//fSTGjZsmDrppJOqlAgEe/mUUiovL0+dcsop6qyzzlJer/eEyxZs5auJhMXq8h0pYRk0aJC64YYbjrtcYZ2w7Ny5U61cufKot5KSEjVnzhyl67rKy8urcH6rVq3UhAkTjnj9X375RQGH/GL3Wbt2rQLUsmXLKmwfOHDgCf3SgqV8B3r33XeVw+FQO3bsOOFy7WN1+davX68A9d5771XYfskll6jLL7885Mt3OKZpHtL2fDysLtu4cePKvzHuuwFK13XVr1+/EypbMJTvcDwej4qKilIffvjhcZdrn2ApX35+vkpPT1cDBw6slkRsn2Apn1I1k7BYXb7vv/9eASo3N7fC9i5duqgHH3zwuMtVe42BFkhMTCQxMfGYxxUXFwOg6xW79Oi6jmmaRzxPlS3D5PF4qnRdm8121OtWltXlO9CUKVM477zzaNCgwTGPrSyry9esWTNSUlJYvXp1he1ZWVkMHTr0mHEdi9XlO5zly5fj8/lo1KhRpc85HKvLds8993DttddW2Na5c2eee+45hg0bdsy4jsXq8h3tvKqeczjBUL78/HyGDBmCy+VixowZREREVCb0SgmG8tUkq8vXo0cPHA4Hs2fP5pJLLgEgOzubZcuW8dRTT1WqDEd64Tpv586dqn79+uqCCy5QmZmZavXq1erf//63cjgcKjMzUyml1Ndff63efPNNtXTpUrVhwwb19ddfq44dO6o+ffqUX+fvv/9Wbdu2VfPnz1dKKeX1elWrVq3UaaedpubPn6/Wrl2rnnnmGaVpmvr6669Dvnz7rFmzRmmapmbOnFlrZTpQTZbvueeeU263W33yySdqzZo16v7771cRERG1PhKjJsq3du1a9cgjj6iMjIzyc9q1a6e6d+9ea0Nja/pv80BYNEqoJsq3bt069cQTT6iFCxeqTZs2qd9//10NHz5cJSQkqO3bt4d8+fLz89Upp5yiOnfurNauXVth2H1tDtuuyb/P7OxstWTJEvX666+Xj9RZsmSJ2r17d1iUb8yYMSo1NVXNmTNHLV68WA0YMECGNVeXjIwMdeaZZ6qEhAQVGxurevfurb755pvy/T/88INKT09XcXFxKiIiQrVu3VrdfffdFarxNmzYoAD1448/lm/LyspSF1xwgUpKSlJRUVGqS5cuhwxzrg01VT6llBo/frxKTU1VhmHUUmkOVZPlmzBhgkpNTVVRUVEqPT1d/fLLL7VUqv1qonybN29Wp59+ukpISFBOp1O1bNlS3XrrrbX6hllTZTscKxIWpWqmfFu3blVDhw5VSUlJyuFwqNTUVHX55ZerVatW1XLpaqZ8+5pJDnfbsGFDyJdPqSP3M3nrrbdqr3Cq5spXUlKibr75ZpWQkKAiIyPVueeeqzZv3nxCsWpKldXtCCGEEEIEKZmHRQghhBBBTxIWIYQQQgQ9SViEEEIIEfQkYRFCCCFE0JOERQghhBBBTxIWIYQQQgQ9SViEEEIIEfQkYRFCCCFE0JOERQghhBBBTxIWIYQQQgQ9SViEEEIIEfQkYRFCCCFE0Pt/B6Wwe67ZwwkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the neighbors for unit 249 in county 87\n"
     ]
    },
    {
     "data": {
      "image/png": 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T7DNEnpbYLRKW0hq1wyAiakwIwFIQEMv5uJUMJScnY/78+di2bRu2bduGkSNHYsKECdi9ezcAwGq1YujQoZg/f36zzzl79mwsWrQIf/3rX7Fnzx5MnToVt912G3bu3Ok65rPPPsOTTz6JWbNmYefOnbjmmmswduxY5OfnuxO+17HPEHlaZFwoSo5XqR0GEVFjJXlATHe1o/AISYiWzPV9TnR0NP785z/jwQcfdO07evQoUlNTsXPnTvTv3/+Sj09MTMSsWbMwffp0175bb70V4eHh+OSTTwAAgwcPxhVXXIGFCxe6junVqxduvfVWzJs3r9mxWiwWREZGoqKiAmazudmPa66Cg+U4dagCV4zx7+pC8i3ffrAHST2ikDY4ARqtF1q2yw4D0V08f14iClzWUufM1hHxbfJ03v78bvE7qyzLWLx4MaxWKzIzM1scgM1mg8lkarAvJCQEGzZsAADU1dVh+/btGD16dINjRo8ejY0bN1723BaLpcHmTc4+Q6wZIs8acns35Hx7HCcPlHvnCYRo2fpHRBS8qkvbLBFqC24nQ7m5uQgPD4fRaMTUqVOxdOlS9O7du8UBjBkzBq+//jry8vKgKArWrFmDL7/8EoWFhQCAkpISyLKM+PiGhR4fH49Tp05d8tzz5s1DZGSka0tJSWlxnM0hFAGNlskQeVao2YDbfncFtq886p3+QzojUHHC8+closBkLQHCYtWOwqPcTobS0tKQk5OD7OxsTJs2DZMnT8aePXtaHMCCBQvQvXt39OzZEwaDATNmzMCUKVOg1TacvOnCGhchLl8LM3PmTFRUVLi248ePtzjO5lAUzkBN3mEK02P4xDRs/OIQfv7ew69jo9mZEBERNUdtBRAarXYUHuX2R7fBYEC3bt0waNAgzJs3D/369cOCBQtaHEBcXByWLVsGq9WKY8eOYd++fQgPD0dqqnPyptjYWGi12ka1QMXFxY1qiy5kNBpdI9/qN28SCjjPEHlNdGIYRtyXhiM/l6DwYLnn5h+S65xzhBARBalW12MIIWCz2VodiMlkQlJSEhwOB7744gtMmDABgDP5GjhwINasWdPg+DVr1mDIkCGtfl5PUrhqPXmZKUyPrgPisOG/eVi5KBeWEg80mzlsgM50+eOIiAKUW6vWP/fccxg7dixSUlJQWVmJxYsXY+3ata55gcrKypCfn4+CggIAwP79+wEACQkJSEhIAABMmjQJSUlJrlFgmzdvxsmTJ9G/f3+cPHkSc+fOhaIo+P3vf+963qeeegpZWVkYNGgQMjMz8fbbbyM/Px9Tp05tfQl4kJDZZ4i8r++IZPQdkYwjv5Rg78ZCDL6llSPBWDNEREHOrWSoqKgIWVlZKCwsRGRkJDIyMrBq1SqMGjUKALB8+XJMmTLFdfzEiRMBAHPmzMHcuXMBAPn5+dBozlVI1dbWYvbs2Th8+DDCw8Nx00034eOPP0ZUVJTrmLvvvhulpaV48cUXUVhYiD59+mDFihXo1Mm3hrBzOQ5qS5FxITi5zxNrlwlAw85uRBS8Wj3PkD/x9jwFB7Y6+zX1uDLB4+cmupDsUPDlGztxXVZPtEsIa/mJSg8FxAyyRNRGSg463zPacCoZn51niBpTZNGg1ovIm7Q6DUbc3xPff7QP5cXVaodDRMGiXSfnKvUBhJ/cHuRMhthMRm0nukMYht3VHdtWHFU7FCIKFlq9c/h0AGEy5EGcdJHUEN/ZjKoztag4zdohIqKWYDLkQYosIDEZIhWMnNQL3324F3W1DrVDIaJgEN4esBSqHYXHMBnyII4mI7WYY0LQpX8cig57d/09IiIAgDECqLOqHYXHMBnyIMFkiFSk02sgOwKrHZ+IfFyADEhnMuRBbCYjNSX2aId9mwqxadkhlBZUNf+BbTg8logCiM4A2AOjryKTIQ9iMxmpKbpDGDr3i4VsV7DmvT3YuSa/eQ8MkG92RNRGqk4DJXnOmesNrZjjzIe4NQM1XZrC5ThIZT2v7gBc7VwzcPU7u1FeVI2o+FC1wyKiQOGwATYLENtd7Ug8ijVDHsSh9eQrJElCx97RKDhYDkURsFXb1Q6JiAJB+fGAnLGeNUMepMhctZ58R9rVCfj6rV+wP/sUFFnghim9EBnHWiIiaoUA7WPIZMiD2GeIfIlWp0FEjAkAoNdrUVcrqxwREZFvYjOZBwn2GSIfc919PXHdfT1hCtfjyM8laodDRP7OGAFUl6kdhccxGfIgRWEzGfmmQTd1RuHBcgiOHCOi1ghvD1gD74sVkyEPcjaTsUjJN+kMWhTklaOmqk7tUIjIn5kigZozakfhUfzk9iAhK2wmI5814r40HNhShO8/2gdF5kzVRNRCEfEB11TGZMiDFJkdqMl3hUUaMeK+NLTvFIFffjihdjhE5M+UwFoUmsmQBymcZ4h8nHP+oRic2HcG5UWNp9GvrbJj7w8HUGvlvEREdAlhcQHVd4hD6z1IUbg2Gfm++FQzBk/ogvX/OYBuA9vj0JYSCKUIRpMGhUdqkD40Dqv/lo2rbk1Du6R20OgkGEx8qyCi84RGAyUHgbBYtSPxCL7DeZBgMxn5ibiUCAy5vRtyvjuO4fdnICLGhOqKOoSaDZA0EnqU1GDt+9tQcUZBQooJo6YNVjtkIiKvYTLkQZx0kfxJTFI4rp/Uy3U7LMro+tkcG4LrplyB6vJq5Kw6iM3/zQEUGYVHqtF7eAo69kuCKUyvQtRE5DMMoUBdtfN/P8c+Qx4kBDjPEAWMiNgwxHeLw3UPXYm4ztFIGZCK6x4chJryKnz5542N+hzl78xH4b5TKkVLRG0uPB6oKlI7Co9gzRARXZLBpEOXKzu6bve7qQ+iOhzHhk92Ii4lDB0HpMAUbsDGZccQFqnF+J4JqKm0oa5GRmR7///GSEQXodECIjCm6WAyRERu6zQgBVFJMdj0313Y9trP0GqBYbclofyUFZ+/tAF6owTZLiOlezhkRUKf0b1hDNNBb9CqHToReVKAzGrPZIiIWiSyfShGTx0ES0ktouKdNUD1y31IkoSqM7UoO1mJnSvzsH3mT+gzNBbD7u0DrZat80TkW5gMEVGLabQaVyIEOJOgeuHtTAhvZ0JSrxgUHapA7urdWP23bIx88EqYwtn5migghMUCVcXONcv8GJMhIvIqrVaDxB7t0KH7UBzcdBjfvb0Fp0/Z0SczBoNu66t2eETUGiFRQOkhv0+GWF9NRG1CkiR0H9IVV0/sB6tFwZmiGrVDIiICwGSIiNpYTGI4xs/oA51WcfUxIiJSE5MhImpzST1jsT+nGpVltWqHQkStUZ4PhMaoHUWrMRkiojZXa7UjJEyDiGiT2qEQUUudPgAYwp39hvwckyEianP2Wgei47RY+89slJ6oVDscInKH7ABO5QLtOjsXbA0ATIaIqM1FxYdh4M1pqDgjsPu7A+w7ROQPKoucI8dKDwLxfQCdQe2IPIbJEBGpIrFXe4x6dCCO7asEmAsR+bayI4BWD8R0Bdr3BKTAWoeTyRARqSbUbEB0vAHL/rwRu9fsVTscIroYRQ6YJrGmMBkiItVIkoRxTw7BgNFdUHioHPt/PAS7TVY7LCI6n2wPqCaxpjAZIiLVdeqfgBBzCI7tKsH2pT+rHQ4Rna+yEAhPUDsKr3IrGVq4cCEyMjJgNpthNpuRmZmJlStXuu5fsmQJxowZg9jYWEiShJycnGad980330RaWhpCQkKQkpKC3/72t6itPTf/yNy5cyFJUoMtISGwfzFEwUSSJAy9tz+ue2gQDu+y4ERugdohEVE91gw1lJycjPnz52Pbtm3Ytm0bRo4ciQkTJmD37t0AAKvViqFDh2L+/PnNPuenn36KZ599FnPmzMHevXvx7rvv4rPPPsPMmTMbHJeeno7CwkLXlpub607oROQH9AYtxj91NbatOKJ2KEQURNxaqHX8+PENbr/88stYuHAhsrOzkZ6ejqysLADA0aNHm33OTZs2YejQobj33nsBAJ07d8Y999yDLVu2NAxUp2NtEFEQiIg2QW+QYC2vRVgUJ2UkIu9rcZ8hWZaxePFiWK1WZGZmtjiAYcOGYfv27a7k5/Dhw1ixYgXGjRvX4Li8vDwkJiYiNTUVEydOxOHDhy97bpvNBovF0mDzFkURgTbSkEg1mXf2xg/v7cDyv2zE5y9twOHNl/97JyJqKbdqhgAgNzcXmZmZqK2tRXh4OJYuXYrevXu3OICJEyfi9OnTGDZsGIQQcDgcmDZtGp599lnXMYMHD8ZHH32EHj16oKioCC+99BKGDBmC3bt3Iybm4muizJs3Dy+88EKLY3OHkAU0WmZDRJ4QnRyFm58aAiEEFIfAqr9vRpfBXdQOiyjwVRUDjlogqqPakbQpt2uG0tLSkJOTg+zsbEybNg2TJ0/Gnj17WhzA2rVr8fLLL+Ott97Cjh07sGTJEnz11Vf44x//6Dpm7Nix+NWvfoW+ffvihhtuwNdffw0A+PDDDy957pkzZ6KiosK1HT9+vMVxXo6iCGg0TIaIPEmSJNhqHAgJ48BXIq87cwzQ6ICwOKB4L1DrvdYUX+N2zZDBYEC3bt0AAIMGDcLWrVuxYMECLFq0qEUBPP/888jKysJDDz0EAOjbty+sViseeeQRzJo1CxpN4zfBsLAw9O3bF3l5eZc8t9FohNFobFFc7lIUAYk1Q0QeV1NVh5AwrdphEAU+xXFuYsX2vQBLAWA9DYS0UzeuNtDqr1tCCNhsthY/vrq6ulHCo9VqIYS46HpFNpsNe/fuRYcOHVr8vJ4mZNYMEXlDTYUNJiZDRN6lyI2X2DAnOpffCOCZp+u5VTP03HPPYezYsUhJSUFlZSUWL16MtWvXYtWqVQCAsrIy5Ofno6DAOUfI/v37AQAJCQmukWCTJk1CUlIS5s2bB8A5Qu3111/HgAEDMHjwYBw8eBDPP/88brnlFmi1zjfAp59+GuPHj0fHjh1RXFyMl156CRaLBZMnT/ZMKXgAm8mIvMNSUARzh1i1wyAKbBXHgcgUtaNQjVvJUFFREbKyslBYWIjIyEhkZGRg1apVGDVqFABg+fLlmDJliuv4iRMnAgDmzJmDuXPnAgDy8/Mb1ATNnj0bkiRh9uzZOHnyJOLi4jB+/Hi8/PLLrmNOnDiBe+65ByUlJYiLi8PVV1+N7OxsdOrUqcUX7mmKLCBp2a+ByNNqK20wJ7ZXOwyiwOawORdiDVKSuFhbVACyWCyIjIxERUUFzGazZ89dUoOcb49j+MQeHj0vUbArOHAGBb8cwaA7rlA7FKLAZCkANHogPE7tSC7Km5/fANcm8xhF4dB6Im/QiDpUlDku2oeQiFqh4iQgaX06EWoLTIY8RLDPEJFXlJ0ox/EDViz7UzaO7chXOxyiwFFx0tk0FhGvdiSqYzLkIc4+Q0yGiDyt9/VpGDejP+pqHVj/OWeiJmq16jLg9H5AHwKEsz8ewGTIYziajMh74jq3w53PD0OH1DD88M/NqLXa1Q6JyD9VnnKuQh+XFhRD5puLyZCHCPYZIvIqjUZCx97ROHnEBp2eb11ELVJnZbNYE/iO4iGKLCCxZojIqxx1CkLDNfj+nS0oL6hQOxwiChBuL8dBTWMzGZH39bquO3pd1x2n8yvx1d+2Y9ivUtF5oO/MN0ZE/ok1Qx6icNV6ojYT1zEC97wwHPs2FuD0kTNqh0Pke4Rwzh9Unu/82bVfUS8mH8ZkyEMEkyGiNqXVa3DN/QOw6fM9yNt4SO1wiHyHbAdO5QKmKCA8ASg7DJQeAor3BfWSG5fCZMhD2ExG1PbC2pkw7slM7NtUpHYoRL6j7AiQ0BcwhAI6g3Ox1ZiuQPuegN6kdnQ+icmQhygKO1ATqUGr1yAiSoczBZVqh0LkGyRN4xXo6ZKYDHkIm8mI1NNzRHfsW7tf7TCI1Fd+HIhMUjsKv8NkyEPYTEaknvguUSg+WQehcP0yCmJCOOcR0oeoHYnfYTLkIYqicDkOIpVIkoSQcA3qah2w22S1wyFSR0mes28QuY3JkIcIWUCjYXESqaXHVYlY/lo2/vPiBigyhw9TkKmrdtYIafVqR+KX+OntIbLMZjIiNXUe2BF3zB6G8EgtZAebyyjIWAqAKA6bbynOQO0hXJuMSF2yQ8HGxb/AYNJAb9SqHQ5R2+LosVZhzZCHKLJgnyEiFe39qQCnj1kwcFwPtUMhIj/DmiEP4WgyIvWUHC3F/g2HMeL+dMR0ilE7HKK2VVcN6DiZYmuwZshDBJMhItVIWi1MYTomQhScKgs5t1ArMRnyEDaTEalIo8PJw7U4lH1E7UiI2h4XX201JkMewmYyIvVExYdCCMAcH6V2KERtq+IkEJGgdhR+j8mQh3A0GZF6tDoNrr0rFT+v5pIcFGTqrIAxQu0o/B6TIQ9RZC7USqSW/Jzj2L/pJAbc1EvtUIjaTsVJ9hXyECZDHqJwoVYi1bRLiYVGA4RGGdUOhajtOGoBQ5jaUQQEJkMewj5DROqJiAlBeLQJu787oHYoROSHmAx5iGDNEJGqSgpqkTG2t9phEJEfYjLkIYrCPkNEagoN12Lv9/vUDoOI/BCTIQ9xNpOxOInUUHTwNI7uq0HGjelqh0LUNiqLAFOk2lEEDH56e4iQFTaTEalk0xcHMPxXHVk7S8GjthwIi1U7ioDBZMhD2IGaSD1xHXSISmqndhhEbcNWxVohD2My5CFcjoNIPT2vS8MvnHCRgoX1NBDKWiFPYjLkIQpnoCZSjbl9BJvIKHhEJDgXZyWPYTLkIUJmMxmRWmqq6gAAtVV2lSMhagP6EMBeDdhr1Y4kYDAZ8hD2GSJST1ikERIUrPtgm9qhELWNuDSg9CAghNqRBAQmQx4iBFhNT6QSrU6DHlcnQuL0FhQobFVA8V6g9JBzK8lz7jtf+15A2WF14gswOrUDICLyhK6DU5G7rkDtMIhaTwig/BgQf8G8WWVHAIcNCItx3tZo2z62AMWvUUQUMCIitTh9tELtMIguTgjgzLFzNT7WksZNXaf3A3G9Gj82OhVw1AAWdp72NLeSoYULFyIjIwNmsxlmsxmZmZlYuXKl6/4lS5ZgzJgxiI2NhSRJyMnJadZ533zzTaSlpSEkJAQpKSn47W9/i9rahh3D3nrrLaSmpsJkMmHgwIFYv369O6ETURDomJGI9Z/+onYYRE1z2IBTuc7RYDFdnZtWD5w5ci45Kj0EhLcHLtbkG5kMSBqg5KCziUxnattrCFBuNZMlJydj/vz56NatGwDgww8/xIQJE7Bz506kp6fDarVi6NChuPPOO/Hwww8365yffvopnn32Wbz33nsYMmQIDhw4gAceeAAA8MYbbwAAPvvsMzz55JN46623MHToUCxatAhjx47Fnj170LFjR3cugYgCmCwLpA/l/Cvko8oOAx0yGu4zRbo/gWJEvHMjj3ErGRo/fnyD2y+//DIWLlyI7OxspKenIysrCwBw9OjRZp9z06ZNGDp0KO69914AQOfOnXHPPfdgy5YtrmNef/11PPjgg3jooYcAOGuSVq9ejYULF2LevHnuXAIRBShZVlB8qBjdhnRTOxSixuqsgNGsdhR0ES3uMyTLMhYvXgyr1YrMzMwWBzBs2DBs377dlfwcPnwYK1aswLhx4wAAdXV12L59O0aPHt3gcaNHj8bGjRsveW6bzQaLxdJgI6LAVHqiCiXHrYhP5TIF5IMqTwHmRLWjoItwezRZbm4uMjMzUVtbi/DwcCxduhS9e/ducQATJ07E6dOnMWzYMAgh4HA4MG3aNDz77LMAgJKSEsiyjPj4hlWC8fHxOHXq1CXPPW/ePLzwwgstjo2I/MeRrUfQa2gStDqOCyEfJXH6FV/l9rtGWloacnJykJ2djWnTpmHy5MnYs2dPiwNYu3YtXn75Zbz11lvYsWMHlixZgq+++gp//OMfGxwnXfAiEkI02nehmTNnoqKiwrUdP368xXESkW/T6rQAOAEd+SgmQj7N7Zohg8Hg6kA9aNAgbN26FQsWLMCiRYtaFMDzzz+PrKwsV3+gvn37wmq14pFHHsGsWbMQGxsLrVbbqBaouLi4UW3RhYxGI4xGY4viIiL/kr+nHBnXsRmCiNzX6vpkIQRsNluLH19dXQ3NBUMItVothBAQQsBgMGDgwIFYs2ZNg2PWrFmDIUOGtPh5iSiwdOoTDSEraodB1DQum+HT3KoZeu655zB27FikpKSgsrISixcvxtq1a7Fq1SoAQFlZGfLz81FQ4JwFdv/+/QCAhIQEJCQkAAAmTZqEpKQk1yiw8ePH4/XXX8eAAQMwePBgHDx4EM8//zxuueUWaLXO2TWfeuopZGVlYdCgQcjMzMTbb7+N/Px8TJ061TOlQER+r/9NPfG/1zbCZDYhJSNZ7XCIyI+4lQwVFRUhKysLhYWFiIyMREZGBlatWoVRo0YBAJYvX44pU6a4jp84cSIAYM6cOZg7dy4AID8/v0FN0OzZsyFJEmbPno2TJ08iLi4O48ePx8svv+w65u6770ZpaSlefPFFFBYWok+fPlixYgU6derU4gsnosCi1WnQpV80youqkaJ2METkVyQhgqfuzmKxIDIyEhUVFTCbPTvfw9dv/YJxv8m4/IFE5DX/e20jbv5tJhdNJt9jqwJslYC5g9qR+CVvfn4DXJuMiAKIwSih8OAZFOwtUjsUooaM4UAt183zVVy1noh8wv61+7E7uwRG0wXf0QSACyp6NBoJGi2g1UrQ6CTn/1rg4K4aHNyVg6hoDe57hcsVkI9p18m59lhMV7UjoQswGSIin1BTUY2rbuuN5LR2lzxOCAGhCMiygOJQIDsEFFmB7FDQZ7SA7BAwhfGtjXyQPsS5DlnFSSAySe1o6DxsJiMin6AoAppm9PWRJAkarQZ6gxbGUD1CzQaEtzMhMi4U7RLCEJscjvB2XMm7pY4ePQpJkpCTk+P2Y1evXo2rr74aERERiIuLw69+9SscOXLEdf8DDzwASZIabenp6R68Ah8XFuucgLHqtNqR0HmYDBGRTxAC7Pjsxw4fPowJEyZg5MiRyMnJwerVq1FSUoLbb7/ddcyCBQtQWFjo2o4fP47o6GjceeedKkauAnMiYK8GqsvUjoTOYjLkAYoiONM6USsJRUDiOxIURcGrr76Kbt26wWg0omPHjq6pRnJzczFy5EiEhIQgJiYGjzzyCKqqqlyPHTFiBJ588skG57v11lvxwAMPuG537twZr7zyCn79618jIiICHTt2xNtvv+26PzU1FQAwYMAASJKEESNG4Mcff4Rer2+0EsDvfvc7DB8+HACwY8cOyLKMl156CV27dsUVV1yBp59+Gj///DPsdjsAIDIy0jXvXEJCArZt24YzZ840mJIlaLTrBNRVASV5zn5EFScAhZOGqoVvPR4gZAGNltmQP5g3bx4kSXJ9YNjtdjzzzDPo27cvwsLCkJiYiEmTJrkmDqW2oyhoVjNZoJs5cyZeffVVPP/889izZw/+9a9/IT4+HtXV1bjxxhvRrl07bN26Ff/973/x7bffYsaMGW4/x2uvvYZBgwZh586d+M1vfoNp06Zh3759AIAtW7YAAL799lsUFhZiyZIlGD58OLp06YKPP/7YdQ6Hw4FPPvnElcgMGjQIWq0W77//PmRZRkVFBT7++GOMHj0aer2+yTjeffdd3HDDDcE7Z1xURyC2u7NDdWgsUH7UmRidOap2ZMFHBJGKigoBQFRUVHj0vHW1DrH6nVyPnpM8b8uWLaJz584iIyNDPPHEE0IIIcrLy8UNN9wgPvvsM7Fv3z6xadMmMXjwYDFw4EB1gw1C2f/eJorzLWqHoSqLxSKMRqN45513Gt339ttvi3bt2omqqirXvq+//lpoNBpx6tQpIYQQ1157reu1XW/ChAli8uTJrtudOnUS999/v+u2oiiiffv2YuHChUIIIY4cOSIAiJ07dzY4z6uvvip69erlur1s2TIRHh7eIJ5169aJ9u3bC61WKwCIzMxMcebMmSavtaCgQGi1WvHZZ59dskyCUq1FiDPH1I7Cp3jr87sea4Y8QATPvJV+q6qqCvfddx/eeecdtGt3brRSZGQk1qxZg7vuugtpaWm4+uqr8de//hXbt29Hfn6+ihEHH0U0rwN1INu7dy9sNhuuv/76Ju/r168fwsLCXPuGDh0KRVFcSx81V0bGuQliJUlCQkICiouLL/mYBx54AAcPHkR2djYA4L333sNdd93liufUqVN46KGHMHnyZGzduhXr1q2DwWDAHXfc0eR75AcffICoqCjceuutbsUeFIwRgGxXO4qgwmTIA/RGLex1bOv1ZdOnT8e4ceNwww03XPbYiooKSJKEqKgo7wdGLkJxfjAHs5CQkIveJ4S4aPnU79doNI0Sj/r+Oue7sNlKkiQol+mv0r59e4wfPx7vv/8+iouLsWLFCvz617923f/3v/8dZrMZf/rTnzBgwAAMHz4cn3zyCb777jts3ry50bW89957yMrKgsFguOTzBqXqMsAUpXYUQYXJkAdIksTaIR+2ePFi7Nixw7U48KXU1tbi2Wefxb333uuVKd/p4oRA0Pe96969O0JCQvDdd981uq93797IycmB1Wp17fvpp5+g0WjQo0cPAEBcXBwKCwtd98uyjF27drkVQ31yIstyo/seeughLF68GIsWLULXrl0xdOhQ133V1dWuxbXr1d++MNFat24dDh48iAcffNCt2IJGdSkQFqN2FEGFyZAHKLICRWYy5IuOHz+OJ554Ap988glMpkvPPWO32zFx4kQoioK33nqrjSKkehxNBphMJjzzzDP4/e9/j48++giHDh1CdnY23n33Xdx3330wmUyYPHkydu3ahR9++AGPPfYYsrKyEB/vnG175MiR+Prrr/H1119j3759+M1vfoPy8nK3Ymjfvj1CQkKwatUqFBUVoaLi3BISY8aMQWRkJF566aVGI8DGjRuHrVu34sUXX0ReXh527NiBKVOmoFOnThgwYECDY999910MHjwYffr0aVlBBTJ+sVZFkL/1eIbDrsAU1vRoCVLX9u3bUVxcjIEDB0Kn00Gn02HdunX4f//v/0Gn07m+/drtdtx11104cuQI1qxZw1ohFShsJgMAPP/88/jd736HP/zhD+jVqxfuvvtuFBcXIzQ0FKtXr0ZZWRmuvPJK3HHHHbj++uvxt7/9zfXYX//615g8eTImTZqEa6+9Fqmpqbjuuuvcen6dTof/9//+HxYtWoTExERMmDDBdZ9Go8EDDzwAWZYxadKkBo8bOXIk/vWvf2HZsmUYMGAAbrzxRhiNRqxatapB819FRQW++OIL1gpdzJmjQFSQjq5TEVet94Baqx0b/puHGx7o7bFzkmdUVlbi2LFjDfZNmTIFPXv2xDPPPIM+ffq4EqG8vDz88MMPiIuLUyna4Lbu/a24YkJfRERz9mhf9vDDD6OoqAjLly9XO5TAxLXLmuTtVeu5gI8HKLKANsj7OviqiIiIRlXxYWFhiImJQZ8+feBwOHDHHXdgx44d+OqrryDLsmtiuejoaHbubEOKwvm6fFlFRQW2bt2KTz/9FF9++aXa4RB5FJMhD5AdCjRatjj6oxMnTri+4fbv37/BfT/88ANGjBjR9kEFKSHYTObLJkyYgC1btuDRRx/FqFGj1A4ncIW0A6qKgfD2akcSVJgMeYAiC2h0fBP3F2vXrnX93LlzZ44E9BGimQu1kjrO/7shLwqNBk7vZzLUxlid4QGKzJohotZSFAT9aDIiAIA5CThz7PLHkcfwrccD2GeIqPWEwlXriQAAxnBAowOsJWpHEjTYTOYBiixgKa3Fif1nYDBpoTdqYTDpoD/7M/tBEF2eUBQ2kxHVizxbO6TROvsRkVcxGfKAyPYhSE5rh5LjlairlWGvdaDOJsNeK8NRJzc5h5YkOZfx0Jt0MJi0Z5MoZwJVn0gZzru/fr9Wx8o8CkyKkFgzRHS+dp2A4r1MhtoAkyEPMJh06D0s0a3HKIqA3XY2cap1Jk51NgfsZ5Opmso61NU6zu537rPXyji08zSm/2Okl66ESD3OGaiZDBE10C7VORFju85qRxLQmAypRKORYAzRwRjS/F+BtdwGmct+UIByDq1XOwoiH6M3AbJD7SgCHttc/IQQAv95ZSu6DuDsyBS42L+OqAn8u/A61gz5iVqrHWFRRvTM7KB2KERtTtjtKP7LX2A7fASasDBE3jIe4ddcA0nPNQGJqPWYDPmJosMWpA1OUDsMIlWUfvABDN26IX7mTDjKymD53/9w/NFHYezVC1G33QZjt25qh0hEfozNZH6gtsqOr9/6BZHtQy5/MFGAsZ88iZqdOYi64w4AgC46GtGTJyPl3XdhvvFGnPnXv5H/8CM48+9/Q66oaPR4pboaoq6urcMmIj/CmiE/cPjn00jsHoXOfWPVDoWozZW+9z7inni8UX8iSZIQ0rcvQvr2hVJXh6rvf0Dh7OchGQwIv+46CIcd1ZuyYVm5Ekn/bwEiuM4c+SMHE/m2wGTIx53cfwb5u8tw/eReaodCpIq6E8dh7NHjksdoDAaYbxwD841jYC8uhnXjRkh6PeJ++yQMXbqgycm+iPxBaR7QvrfaUQQ8NpP5MEUR2LriKIbe0Q3mWDaRUXCSNFq3khl9+/aIuvVWRI4bB31CAiJGj4J14yYvRkjkJaWHnPMMcTSZ1zEZ8mFHfylBbFI4IqJNaodCpBpdXBwcp0+3+PHCZoMmhF8myM/UVgD6UMAQqnYkQYHJkA+TNBJ0Rv6KKLjpk5NhP3GixY+3rFyF0CsHeTAiojZQVQyYOZVKW+EnrQ9TZAXh7VgrRMFNFxsLR0lpix9vXb8e1p82ejAiIi8TAgCbxtoSO1D7MEUWOL6nDH2GJ6kdCpHX1ezej+PTPj63QwgYOneGpNMibNg1LT5vp08+xokZj3kgQqI2UnoQiOqkdhRBhcmQD6uuqIO1wsYFLCk4SBJSFr7VYFf1jh0oWbQIcU880fLTmkyQQkJQvWMnQq8Y0NooiTzrzDFArgOk8xpqwuIAnUG9mIIQm8l8WJ9rk1B0xIIjv5SoHQqR10loPGIs9Ior0HHRolYtuyFpNEh89VWU/vOfrQmPyPNOHwBCY4DY7kBM13NbSJTakQUdJkM+TKvToPuV8ZAditqhEPk1xVIByLLaYRCdU3bY2UHaGK52JAQ3k6GFCxciIyMDZrMZZrMZmZmZWLlypev+JUuWYMyYMYiNjYUkScjJybnsOUeMGAFJkhpt48aNcx0zd+7cRvcnJATHOl0j7kvD7vUn1Q6DyOuEFzuM6jp0gCYiArX793vtOYiaTVGcnaSNEWpHQme5lQwlJydj/vz52LZtG7Zt24aRI0diwoQJ2L17NwDAarVi6NChmD9/frPPuWTJEhQWFrq2Xbt2QavV4s4772xwXHp6eoPjcnNz3QndbxlMOkTGhaLoiEXtUIj8Vs327XCUlMCQnKx2KERA+TF2kPYxbnWgHj9+fIPbL7/8MhYuXIjs7Gykp6cjKysLAHD06NFmnzM6OrrB7cWLFyM0NLRRMqTT6YKmNuhCV45Lxddv/Yy0wQnof0NHtcMh8oqm+gx5QtH8V1Hx5ZdI/XIZNGFhXnkOIrcIBdBy/JIvaXGfIVmWsXjxYlitVmRmZnosoHfffRcTJ05E2AVvWnl5eUhMTERqaiomTpyIw4cPX/ZcNpsNFoulweaPwtsZEZMYjp8+P6h2KER+RQiBsg8+gHzmDHQxMWqHQ0Q+yu3UNDc3F5mZmaitrUV4eDiWLl2K3r09s4jcli1bsGvXLrz77rsN9g8ePBgfffQRevTogaKiIrz00ksYMmQIdu/ejZhLvMHNmzcPL7zwgkdiU1uo2YDBt3RROwwijyj5+RBWLtiMEJ0dAKCRBGTh+T5DkiTBPG4cdPHxkLRaj5+fqMWE4JpjPkQSwr3lnOvq6pCfn4/y8nJ88cUX+Oc//4l169Y1SIiOHj2K1NRU7Ny5E/3792/2uR999FFs3Ljxsv2BrFYrunbtit///vd46qmnLnqczWaDzWZz3bZYLEhJSUFFRQXMZnOz4/IFe34qQK3VjitGs52Z/N/hr7egYPNeDHtxMhRFgVJnBxQBXajnZ1xXamtx/KGHEfv4Y9AYDAhx4z2JyCvKDgPR/HLrDovFgsjISK99frvdTGYwGNCtWzcMGjQI8+bNQ79+/bBgwYJWB1JdXY3FixfjoYceuuyxYWFh6Nu3L/Ly8i55nNFodI18q9/8Va/MDji+p0ztMIg8QrY5UF9Ro9FooDMZvZIIAYDGZELSgjdxYvoMFMx8zivPQeQW9+ogqA20ep4hIUSD2peW+s9//gObzYb777//ssfabDbs3bsXHToEzyJ2kkaCEALH9zEhIv/nsNmh1bddE4EuJgY9tmyGYrXi1Esvt9nzEjWJyZDPcSsZeu6557B+/XocPXoUubm5mDVrFtauXYv77rsPAFBWVoacnBzs2bMHALB//37k5OTg1KlTrnNMmjQJM2fObHTud999F7feemuTfYCefvpprFu3DkeOHMHmzZtxxx13wGKxYPLkyW5drL+75q4e+OpvP3MSRvJ7sq0OOn3bjqaRJAmJr86HJiSkTZ+XqAF7LZfa8EFuvRsVFRUhKysLhYWFiIyMREZGBlatWoVRo0YBAJYvX44pU6a4jp84cSIAYM6cOZg7dy4AID8/HxpNwxzswIED2LBhA7755psmn/fEiRO45557UFJSgri4OFx99dXIzs5Gp07B1X8mJikcSd2joNVx4nDybw6bHVpj2w8tlgwGSCZjmz8vkUvFcefyG+RT3Ho3unCU14UeeOABPPDAA5c8Zu3atY329ejRA5fqx7148eLmhBfwhBCoOF2jdhhEreaw2RFiaPl6Yy1+3tOnoYuJbfPnJQLA5jEfxioGP1JTaYe1ok7tMIhaTa5zQGtq+6aCsMxMVH7zzSW/fLWEvaAAllWrPX5eCjDlx4B2ndWOgprAZMiP2G0yIqK9M+KGqC3JdhlaFWqGtJGR0Ccloa4Zk7Y2l6OsDAXPzYLt0EGcmPEYEyK6OEUGtG3/uqfL43zgfiQyLgRaHSfpIv8n2x3QGtXpROooLob2gmWAWkKuqkLRy6/AfvIk4h6bgdArr0TB7NmQS0qgi4vzQKRE1FZYM+Rnqs7YUHzMP5cVIaonZKFaMhQyaCDO/PvfsBcXt+o8dQcPAloNkv/+N4ReeSUAwJCcjLoTJzwRJgUizjjts5gM+Zkrb07FoZ2n1Q6DqFVkWUBrUKdiOnzoUFR+swZnPv64VeepPXAA4ddeC21EhGufPikZ9pMFrQ2RAhWbUH0WkyE/06FrJITCPyjyb4osVBlaDwCm3r3R8Z/voHb3bghFgWyxQKlremCCcDga3JYrK3H6r39D+RdfoGLpMoQOHNjgfkOnjrAdOHDJ5xeKAsuaNXCcOcP+RUQ+gn2G/IxGq8HOb/Ix5PZuaodC1GKyDFU6UNfTxcZC1z4eB0dch5B+GRCKgHDYoTGFwNijOxynTqHu+AkoVVVI+sufYejcGQBQsXQZtDHRUKzVSJz3CnQX9D0y9e2LkrcWwl5QAH1iYpPPXfHlclRv2YKTjz+BpNf+gvCRI6ExcWAEkZqYDPmZ2ORwdO7beJZuIn/irBlSd1RNzCMPI3baVBjOm7xVqa6GLS8Pcnk5Qvr3R9mHH6L0gw8Qcd11CBsyBEKRITkEoidNavKckiQh8le3w7pxI6LuuKPR/Y4zZ1D+xefo9P77iHvicRy6aRwMnf+JxHnzYUrr4bVrJRXVlAO2SsCcCAiuHuCr2Ezmh84UVWPtv/arHQZRiykKVE+GjF26NEiEAEATGoqQfv2cfYEiIxExejRCB10J26HDOD51GmwH8hB5++2XPK+pRw9Ub93W9J2yDEPHTpD0eugTEpC2fRs6LlqEonnzGjXJUQCoLnMmQuHtgTNHnf+TT2LNkB8afncP7N7ATprkvxQZqky66C5Tz54w9ewJAIj59ZTLHO1k6NQJ+o4pKH79DRi7dkH49TdAGx4GANDGxMBRWuI6VpIk6OLiYOzaFfbCQhhSUjx/EaQORQGqioH2ztcPYrqqGw9dEmuG/FD7TmZUV9i4NAf5LUUBdCrXDHlT3PTpMHTqBLmyCid/+1vYC5xfXiRJgjYsDLX79jV6jKTh23FAKT0IxLLp01+wZsgPmcL1GH5PGn74ZC/GP94fWi3fRMm/yIp/1Ay1RtSvnM1pYYOvwqGxNyF60iTYDh2CZDRAFx/f8GChAEyGAouk4e/UjzAZ8lNxKRGITYnAgc1F6DWkg9rhELlFEZJqky62NV1cHITNhtDBgxH3+GOQ9I1rxITMZCigVJ4CIuIvfxz5DP71+bFBYztjy1eHIcscoUB+RghogiQZ0kZFoftPGxA+bGiTiRAAQFHYTBZI6qyAMeLyx5HP4F+fHzOG6hAWacSRnJLLH0zkQ4QigIslBgFIF3Pp6TCEIrNmiEhF/OvzY5IkYeSkXvjp8zy1QyFyk4DEdZrOUQSToUDhsHFlej/Evz4/V1ZgRftOZrXDIHITEyEKUBUngEhOkeBvmAz5uRP7yhDZPkTtMIiICHAuxspaT7/D0WR+7spxqfjg2Z8w+JYu0OqY2xL5Ja0GxfPnQzIYnR+kGulsh2oJYdcMQ8R116kdITWHvYZNZH6KyZCfC4syIqGLGUd/KUHXKzjVO5E/Spg9G0pVFYSiAAIABKAosBeegmXFCiZD/qLsCBDfW+0oqAWYDAWAzhmxEELtKIiopTQmU5Mr1wtZBjRscvELZYeByCS1o6AWYjIUADp0i8Kx3FJ0G8iaIaJAIul0sG74Ccen/ebsDmdiZOrdG3EzpqsYGblYS5ybuQNgilQ7GmohJkMBoEOXSGz53xHkrj2BPtcmccgyUYDQJySg64qvG+13JUekDiGA8nxArgNCY84txkp+iz1uA4CkkXDzjAwczS3BkZ85ASNRoLMXFqJ8yVK1wwhOjjqgaDcQkQDEdgdCo9WOiDyAyVCA0Om1uO7+nti9vkDtUIjIy5LffAPln32mdhjBp64aKDsEJPQBdEa1oyEPYjIUQMLbmWC3OZD95SG1QyEiLzJ07gxtNGsk2tyZI0D7XmpHQV7AZCjATHhyALavPIaCvHK1QyFqkuDQR/JHVaeByGS1oyAvYTIUYLQ6DRK6RGL3+pP80CGfpMgKJChqh0HkHmM4UFuhdhTkJUyGAtA1d3fHyf1nYC23qR0KUSOyzQ6txESd/Iw+BFAcQHWZ2pGQFzAZCkDtO5mR0jsa21YeUzsUokbk2jpIfOdpPdb8tr3oLs5V6UsOspYowPAtKVBJEswxjWe0JVKbbLNDq/HuB/m8efNw5ZVXIiIiAu3bt8ett96K/fv3X/T4Rx99FJIk4c0332yw/9SpU8jKykJCQgLCwsJwxRVX4PPPP/dq7M3G+cTUYe4AxHYDaspZSxRAmAwFKkVAo+WbJfkeudbu9c/xdevWYfr06cjOzsaaNWvgcDgwevRoWK3WRscuW7YMmzdvRmJiYqP7srKysH//fixfvhy5ubm4/fbbcffdd2Pnzp3evQDyfe06AdWlakdBHsJkKEClD09CWUHjN34itcm2Omi13n2OVatW4YEHHkB6ejr69euH999/H/n5+di+fXuD406ePIkZM2bg008/hV7feLXxTZs24bHHHsNVV12FLl26YPbs2YiKisKOHTu8ewHkJ/iFM1AwGQpQFadrEJMUrnYYRI04bHZo2njx0YoKZ/+O6PPm5lEUBVlZWfi///s/pKenN/m4YcOG4bPPPkNZWRkURcHixYths9kwYsSItgibfF1oNGBl7VAgYDIUoAwmLZvJyCcpdXZovFwzdD4hBJ566ikMGzYMffr0ce1/9dVXodPp8Pjjj1/0sZ999hkcDgdiYmJgNBrx6KOPYunSpejatWtbhE6+LjQaqC1XOwryAC7UGqAMJh3stmq1wyBqxGGzQ9uGifqMGTPwyy+/YMOGDa5927dvx4IFC7Bjx45LLmw8e/ZsnDlzBt9++y1iY2OxbNky3HnnnVi/fj369u3bFuETURtgMhSgFJkdqMk3KTZHm702H3vsMSxfvhw//vgjkpPPzR68fv16FBcXo2PHjq59sizjd7/7Hd58800cPXoUhw4dwt/+9jfs2rXL1YzWr18/rF+/Hn//+9/xj3/8o02ugXwcR/UFBCZDASo00oCfvihE/xs6Xv5gojYk1zm83kwmhMBjjz2GpUuXYu3atUhNTW1wf1ZWFm644YYG+8aMGYOsrCxMmTIFAFBd7axZ1Wga9ibQarVQFM6gTWdxvqeA4FafoYULFyIjIwNmsxlmsxmZmZlYuXKl6/4lS5ZgzJgxiI2NhSRJyMnJuew5R4wYAUmSGm3jxo1rcNxbb72F1NRUmEwmDBw4EOvXr3cn9KATkxQOQ4gOdbUOtUMhakC22aHVebe74vTp0/HJJ5/gX//6FyIiInDq1CmcOnUKNTU1AICYmBj06dOnwabX65GQkIC0tDQAQM+ePdGtWzc8+uij2LJlCw4dOoTXXnsNa9aswa233urV+Imobbn1jpScnIz58+dj27Zt2LZtG0aOHIkJEyZg9+7dAACr1YqhQ4di/vz5zT7nkiVLUFhY6Np27doFrVaLO++803XMZ599hieffBKzZs3Czp07cc0112Ds2LHIz893J/yg0/faZOzbdErtMIgakO0OaHTebVpYuHAhKioqMGLECHTo0MG1ffbZZ80+h16vx4oVKxAXF4fx48cjIyMDH330ET788EPcdNNNXoyeiNqaW81k48ePb3D75ZdfxsKFC5GdnY309HRkZWUBAI4ePdrsc54/1BUAFi9ejNDQ0AbJ0Ouvv44HH3wQDz30EADgzTffxOrVq7Fw4ULMmzfPnUsIKim9orHszZ3IuI4rLZPvcNYMebedrCWLFDf1vtW9e3d88cUXHoiIiHxZi+uqZVnG4sWLYbVakZmZ6bGA3n33XUycOBFhYWEAgLq6Omzfvh2jR49ucNzo0aOxcePGS57LZrPBYrE02IKJKVyPkPDGE8kRqUmx1UGjZ3dFChCSBMh2taOgVnI7GcrNzUV4eDiMRiOmTp2KpUuXonfv3h4JZsuWLdi1a5erBggASkpKIMsy4uPjGxwbHx+PU6cu3QQ0b948REZGuraUlBSPxOlPQiIMOLGP6+eQ75Dr6qBtYrZnIr/ULhUoPaR2FNRKbn89S0tLQ05ODsrLy/HFF19g8uTJWLdunUcSonfffRd9+vTBVVdd1ei+C+cCEUJccn4QAJg5cyaeeuop122LxRJ0CZHdJuPn708guWf05Q8m8qC1sz5CyWlnc5UEQACQJIEaux4DhvH1SAFCkoCQKOfCrSFRKgdDLeV2MmQwGNCtWzcAwKBBg7B161YsWLAAixYtalUg1dXVWLx4MV588cUG+2NjY6HVahvVAhUXFzeqLbqQ0WiE0WhsVVz+rkPXSM43RKooPS1w+1v3Q3Ne/yDZ7oBcY4MuPETFyIg8LCLBWTvEZMhvtXp8qxACNput1YH85z//gc1mw/33399gv8FgwMCBA7FmzZoG+9esWYMhQ4a0+nkDXfrwJOTvKYMsc14UalsCaJAIAYBWr4PBHNZo7h4iIjW5VTP03HPPYezYsUhJSUFlZSUWL16MtWvXYtWqVQCAsrIy5Ofno6CgAACwf/9+AEBCQgISEhIAAJMmTUJSUlKjUWDvvvsubr31VsTExDR63qeeegpZWVkYNGgQMjMz8fbbbyM/Px9Tp051/4qDjDFEhx5XxmPDZ3m49t40tcMhIg8QisKZj32NVg84bIAuuFsj/JVbyVBRURGysrJQWFiIyMhIZGRkYNWqVRg1ahQAYPny5a7ZWwFg4sSJAIA5c+Zg7ty5AID8/PxG3woPHDiADRs24Jtvvmnyee+++26UlpbixRdfRGFhIfr06YMVK1agU6dO7oQftHpmdsC2FUeRvewQrr6VC0wS+T1ZhqRl7ZpPiUwByg4DMXyP9UeSaMmEHH7KYrEgMjISFRUVMJvNaofTpqwVNnzzz9249bcDIGn4jZK87/NHPsQdb09WO4yApNTVoeD/fo/kBW+qHQqdr/QQkyEv8fbnNyf7CBLGEB1KC6pQV+uAMZTDmskzHnh/C9buP43YcCNu7BMPnUYDnUaCViuhY40N5dV1iAo1qB1m4GHNEJFHMRkKElqdBu3iw6A38VdOnrN2/2lIhtMotUn4dPvJs3udNY8v1Sn4avonMOkbdqK2ObQYN3MEorpzZvSWErICSEyGiDyFn4xBQtJIiGofguqKOoS3Ywc/8owEswlFNRJMiMcjw7tAVgQcisA/1h3C7B4Cj11zNZ4a3bDj/prfvYfacqtKEQcIRQZYM0TkMUyGgkhNlR2GEO+uCUXBJbNrDJbtOoGnRvXAw8O7uPafsdbhPz+XwKhv/HpTZAGdkU1nrSFkGZKGf8s+RbYD/J34LX61CCI6gwar3t6ldhgUQOoczvmrDLqGbyU2hwwAMOoav8UoMqANYb+1VhGCNUO+pjwfiOyodhTUQvxrCiJjHu4Dm9WOY7tK1Q6FAkTd2ck89doLkyHn/qaSIVkBdCY21bYKa4Z8jyIDnEzUb/E3F0QkScKEJwdg9/qTlz+YqBkuXjOkAJBg1DXRTKYAuhAmQ60hFIU1Q76Gyalf419TkNGbtKg4XYMgml6KvMjuqhlqOHeVM0kSjZIkwJkMaZkMtY4sQ2ItBJHHsAN1kJEkCfGpZpw6VIEO3aLUDof8XH3N0Iv/24O/fLPftb/IYgMkCZomJvhUhAZaE/sMtYZQFNZEEHkQv1oEoc59Y7H5f4fVDoMCQMeYUAAalNUV4kTlCddml05DyKHoEhvW6DEKNE0mSeQGTrpI5FGsGQpCXfrHYd+mQpScqERscoTa4ZAf+9OvMvDAkM6QlcbNrnERRiS3C23ycRIXGW0V1gz5IHY98GtMhoJUUo92qKmyqx0G+TmdVoOM5Ci1wwg+isKaIV9j7gCUHweiUtSOhFqAf01BqqaqDuAXGSK/JFdUsGbI1xjCAEet2lFQC7FmKEhpNBLKCqxI6RWtdihE1Ey2w0dwesECSAYDYh99RO1w6EJhcUBlERARr3Yk5CYmQ0HKGKaH3shvlqQCISCEYL8hNwhZRtlHH6N6+zYkzJoFfYcOaodETQmJAkrymAz5ITaTBSG7TcbuH09CNNHplcjbtOYIlH34odph+I26Y8dw/De/gSYsFMl//SsTIV+n0QGyQ+0oyE2sGQoyQhHYuOQgeg9LRPo1SWqHQ0HIkJKIusOrYFm1GuYbx6gdjtfV5O5y9vGR4JwoUdIAGgmSVgtIGmdH6LP763+WNBoIhwPWjRtRvW07Ep7/AwzJ/Hv1C5EpgOUk0K6T2pGQG5gMBZm87UWwWe3of0+a2qFQEEt4fjZOPPlb6JMSEdK3r9rheFXRvHkw33jj2VvC2UwoK4BQnP8rCoQiA4oAFNk5bF5WAI0GIX37IHrKFM427U+0OkBhzZC/YTIUZMIijcjbVoxRD7LPBqlH0uuROO8VnHjscST+6U/Qx7dXOySvMaWnI+yaYTCmpqodChFdBL9uBJnwds41oZgIkdq0ZjMS5sxB4axZUGw2tcPxmtCBA1G9davaYRDRJTAZCjI6vdaVEBGpzdglFdGTJ+PU3BcCdvHgsMyrYd20Se0wiOgSmAwFmYqSGiT1aKd2GEQu4dcMg7FHD5S9957aoXiFNjISSnU1hJ0zvgeNAE3sAxmToSCTkGpGXS0795E6LjadQ/QDk2E7fBhV69a1cURtI7R/f9Tk5KgdBrWViHjAUqh2FOQGJkNBRtJIUGR+ayF1KLKAVte4v5okSUiYMwdln3wK26FDKkTmXWHXDEfVj+vVDoPaijECqLOqHQW5gclQEBFCYM17e2CODVE7FApSsqzgYqPENQYDEl95Gade/CPk8vI2jcvbTL17oXbvXrXDoLbG5jK/wWQoiOxadxJFRypw5c2d1Q6FgpTiENBqLz6SURcXh/b/938oeG4WhCNwmnMljQa62FjYi4vVDoXaSrvOQNlhtaOgZmIyFEQkjYQBozshJNygdigUpGRZgeYSyRAAhPRJR+TN41D0pz+1UVRtI2zYMFjXb1A7DGorWh2gyGpHQc3EZCiI2KrtCI1gIkTqUeRL1wzVM990EzShoSj//PM2iKpthA0dAuvGjWqHQW2pXSfgzFG1o6BmYDIUROJSInDqcIXaYVAQkx0KNM2c9z7u8cdR9dNPqN6+3btBtRFdu3aQKy0B1fxHl6EzsnbITzAZCiIpvaORv6cUlWW1aodCQepyfYbOJ2k06PDHl3D6b3+DvaDAy5G1jZCMfqj55Re1w6C2Yq8FNFq1o6BmYDIURCRJwoj7e2Lr10fUDoWClHM0WfOXgtGGh6HDH/+IwtmzoVRXezGythE+/BpU/fij2mFQW7DXOjtQt+usdiTUDEyGgkxCaiQO7Th90cnviLxJcQi3vygbkpMR8+hUFD7/B79fssPUpw9qf/nF76+DmqH8GBDfW+0oqJmYDAUZW7Ud4e2MkNz4dk7kKUozRpM1JWzwVQgdNBAlb73lhajajqTRwJTuTIgowDW3cxz5BCZDQSZvWzE6Z8SqHQYFKUUWLUqGAKDdPfdALi2F5ZtvPBxV24qccAsqvlyudhhEdB4mQ0GmqqwW3Qe1VzsMClKKLC46A3VzxM+ciYolS2FZs8ZvR2UZu3VDXX4+RF2d2qEQ0VlMhoJMiNmAM4X+3xGV/JMsK80eTdYUSa9H4vx5qDt0CMen/QayxeLB6NpO+PDh7Egd6NgvzK8wGQoyaVclYOOSg2qHQUFKcbSsz9D5tFFRiJ06FXFPPIET02egfNmyRh2ShaK06jm8LeKG61G1jslQQJPYL9OfuJUMLVy4EBkZGTCbzTCbzcjMzMTKlStd9y9ZsgRjxoxBbGwsJElCTk5Os85bXl6O6dOno0OHDjCZTOjVqxdWrFjhun/u3LmQJKnBlpCQ4E7odJbsUBDCWahJJYrDDo3OMx1LQ/qkI+Wdt+E4VYTCmc9ByDKU2loUPPMsjk68B9bsbI88jzfoOnSA/dQptcMgorPceldKTk7G/Pnz0a1bNwDAhx9+iAkTJmDnzp1IT0+H1WrF0KFDceedd+Lhhx9u1jnr6uowatQotG/fHp9//jmSk5Nx/PhxRERENDguPT0d3377reu2VsuJrFpC0khctZ5Uo9gd0Og9N8pGYzIhduqjKF+yFCV//zs0YeEIvepKJMydgxMzHoN85gzChw9H5bffwrL6G0AICIcDSX/+E7RRUR6Lo56oq0Ptnj0AAGNaGjQhTf+tSZIEie9hRD7DrXel8ePHN7j98ssvY+HChcjOzkZ6ejqysrIAAEePHm32Od977z2UlZVh48aN0Ov1AIBOnTo1DlSnY22QB+j0GhQdqUBdrQMGE4d+UttS7HboDHqPnzfytltR9sGHkM+cQfSkLEh6PZIWvInyz/6DgpnPIWxIJpJefw0akwllH32M8i+WIObBX3vkuZWaGtTu3Yfa3F9Q+d33CL3qKkAjofT9DyDq6iBkB/SJiTClpyN8+HDo4+MBAEL4dlMetULFCSCcA1X8SYs/DWVZxn//+19YrVZkZma2OIDly5cjMzMT06dPx5dffom4uDjce++9eOaZZxrU/uTl5SExMRFGoxGDBw/GK6+8gi5dulzy3DabDTabzXXb4qedLT3JEKKDOTYEegO/lVLbU+wOaMJCPX5eSZIQM+WBBvu04eFNJjztsu5Hyd/fQsGzMxH3xOPQd+jQ4H4hy6g7lo/qbVtR9cNaaEJCYOrTB5rQUBg6psCUng6hKKjetAmWs90EjD3SYOzaBSn/WAhNaMPrE0LAUVCAml27cerFP8LYozva3XUXtOZIzxYC+QZrqbPztDHi8seSz3A7GcrNzUVmZiZqa2sRHh6OpUuXonfvls+yefjwYXz//fe47777sGLFCuTl5WH69OlwOBz4wx/+AAAYPHgwPvroI/To0QNFRUV46aWXMGTIEOzevRsxMTEXPfe8efPwwgsvtDi2QCVJZ1cP58SL1MYUhwMavedrhtwhSRLiZkxH7Z49KHplHnRxsQgZcAV0sTGo+OoryCWl0CclImTAFUh643WI2lrU7tsHpboGtfsPoHzJUkBREDp4MBL+8Afo4uIu+3z6pCTok5IQMXoUrD/+iJO/exoR11/fRldMXiOEsxbIYQMgAEjOxVmjUtSOjNwkCTfnha+rq0N+fj7Ky8vxxRdf4J///CfWrVvXICE6evQoUlNTsXPnTvTv3/+S5+vRowdqa2tx5MgRV03Q66+/jj//+c8oLCxs8jFWqxVdu3bF73//ezz11FMXPXdTNUMpKSmoqKiA2Wx246oDy7YVRxDdIRxdBlz6TZzI037+KgfRqclISfediT9rDxyAbe9eOErLEDr4KoSkp3v9OYUQkDjayH9VnQZqK5zfLM2JgJ79ML3NYrEgMjLSa5/fbtcMGQwGVwfqQYMGYevWrViwYAEWLVrUogA6dOgAvV7foEmsV69eOHXqFOrq6mAwNB75FBYWhr59+yIvL++S5zYajTAajS2KK5DV1cgwhLCZjNqeIgtodL712jP16AFTjx5t+pxMhPyYbAdsFiC2m9qRkAe1ep4hIUSD2hd3DR06FAcPHoRy3rwgBw4cQIcOHZpMhABnjc/evXvR4YK2fmqe/qM6YtvKo2qHQUGoNctxEPmE2gogzHdqNskz3EqGnnvuOaxfvx5Hjx5Fbm4uZs2ahbVr1+K+++4DAJSVlSEnJwd7zg4t3b9/P3JycnDqvPk0Jk2ahJkzZ7puT5s2DaWlpXjiiSdw4MABfP3113jllVcwffp01zFPP/001q1bhyNHjmDz5s244447YLFYMHny5FZdfLBy1Mkwhqjbb4OCkzMZ4lyv5McM4YCtSu0oyMPcaiYrKipCVlYWCgsLERkZiYyMDKxatQqjRo0C4BwZNmXKFNfxEydOBADMmTMHc+fOBQDk5+dDc97iRCkpKfjmm2/w29/+FhkZGUhKSsITTzyBZ555xnXMiRMncM8996CkpARxcXG4+uqrkZ2d3eQQfLq8khNV6NCNI1mo7SkKWDNE/s1RC+hMakdBHuZ2B2p/5u0OWP5i5aJc9BmehJRe0WqHQkFm07+2oce1PRGTFK52KETuKckDNGf7u0WmAFrWrrcln+tATf6t6IgFNqudiRCpQlEArY7NZORnzhwFojoCQgGqy5gIBSAmQ0Fmz08FuOJGNi+SOmR2oCZ/JDuc8wcBQGSSurGQV/ArWhApL66G3SajY++LT1RJ5E0cTUZ+RwiuQB8EmAwFEY1GQtWZWsgy10QidSgyOJqM/Et1GRDKL5CBju9KQcQcG4J2HcJwprBa7VAoSCkKa4bIz9SWAyFRakdBXsZkKIjIdgXFRy1oF+/5hTKJmoPNZOR3FIfaEVAbYDIUJIQisGLhL+h3fQq0ev7aSR2cZ4j8hqIARbudo8go4HE0WZDYl30KCV0j0fNqLmFC6uEM1OQ3inYB8X0ADV+vwYC/5SBxaGcxug+KVzsMCnJCODvyE/k0SyEQncpEKIjwNx0kLCW1CI1seuFbIiI6j70aMEaoHQW1ISZDQaCmqg5nCq0wmNgqSkREdCEmQ0Gg6IgFg27qrHYYREREPonJUBCoLK1FeDuj2mEQERH5JCZDQcAUrofDzlmniYguq9bC/kJBiMlQENi/+RTSBieoHQYRke+zngbC26sdBbUxJkMB7vieMhzLLYUxlJ2niYiImsJkKMCtXJSLfiNTIHHVZSIioiYxGQpgjjoZOoMGQ+/spnYoREREPottJwFMZ9AiJMIAR50CvVGrdjhERO6RHUBdJWCrBGS7955HowUkLSBJzo2CDpOhAGeOMaGu1sFkiIKHIjs/OBW783/ZDkgawBAG6ENa/2EnBCCUy2znHaPITR/TXJKm6eOF4ryv0fFNXJ8QTd9fv/9yj1GLRgsYzUB4AqDz0gz6Qpz9HcnO//Uh3nke8mlMhgLY3o0F0Oo10Go1qK2yA0283zV4DzzvhtToB0AoAooioNVpoDdqvdIPSQjh/BwRAhDn3VbE2fvP/1m4PiPqfxZn38AddTI2fnHQdU2SBEgaCZIkQaM5+/PZTSNdeFuCdN4xGo107vGu2+eO0dQ/VrrgtgZnn+/cuSUJDW5rNM7gGpxTaurx5+2XLnFfg+PQ5O9ICOfvUbYrUBwCskNpuNkb7mvOMc7t7D67AkV23oYQDV5XphAJKD3k8ddNA5IG0OoBrQHQ6ABDqDNxqK0Aqk557jkk7dn/L9wk5/8aXcPbDY5vZg2EOK8MWWPhHZIEaHXgx2Fw428/gB3LLYXWoMFPX+QBZ7/kNfiuJ87/UTTaf+EXw/oPXtkhYLfJ5w7y8Ju0K/E4e6P+Ax7AuQ97AKj/wHf93PB2z8wO6HpF+wYJVX1C1+i2Up9Q1d8+t+/it537FHHBbUWcO5eswHHeuZ3nP/vcouE5Ic6777wYFSGAC59HNDxOueB89fsbOfv70mgkaHUStDoNNHoNtLr6TTrvZ+dtzdmfdQYtjGH6Jo7RQKtvuE+jk6DVaiD50qKspki1I3AfkyCiNsFkKIDd+GhftUPwCfVJEnzpg5mIiHwGR5MRERFRUGMyREREREGNyRAREREFNSZDREREFNSYDBEREVFQYzJEREREQY3JEBEREQU1JkNEREQU1JgMERERUVBjMkRERERBjckQERERBTUmQ0RERBTUmAwRERFRUGMyREREREFNp3YAbUkIAQCwWCwqR0JERETNVf+5Xf857mlBlQxVVlYCAFJSUlSOhIiIiNxVWVmJyMhIj59XEt5Ks3yQoigoKChAREQEJEny+PktFgtSUlJw/PhxmM1mj5/fX7AczmFZOLEcnFgOTiyHc1gWTpcrByEEKisrkZiYCI3G8z18gqpmSKPRIDk52evPYzabg/pFXY/lcA7Lwonl4MRycGI5nMOycLpUOXijRqgeO1ATERFRUGMyREREREGNyZAHGY1GzJkzB0ajUe1QVMVyOIdl4cRycGI5OLEczmFZOKldDkHVgZqIiIjoQqwZIiIioqDGZIiIiIiCGpMhIiIiCmpMhoiIiCioMRlyw4EDBzBhwgTExsbCbDZj6NCh+OGHH5o8trS0FMnJyZAkCeXl5Zc996ZNmzBy5EiEhYUhKioKI0aMQE1NjYevwHO8VRYjRoyAJEkNtokTJ3rhCjzDm68JwDnr6tixYyFJEpYtW+a5wD3MW+Xw6KOPomvXrggJCUFcXBwmTJiAffv2eeEKPMMb5VBWVobHHnsMaWlpCA0NRceOHfH444+joqLCS1fhGd56Tbz99tsYMWIEzGazW39LavFWOdhsNjz22GOIjY1FWFgYbrnlFpw4ccILV+AZlyuH0tJS3HjjjUhMTITRaERKSgpmzJhx2bVEDx06hNtuuw1xcXEwm8246667UFRU5HZ8TIbcMG7cODgcDnz//ffYvn07+vfvj5tvvhmnTp1qdOyDDz6IjIyMZp1306ZNuPHGGzF69Ghs2bIFW7duxYwZM7wy5bineKssAODhhx9GYWGha1u0aJEnQ/cob5YDALz55pteWTrG07xVDgMHDsT777+PvXv3YvXq1RBCYPTo0ZBl2dOX4BHeKIeCggIUFBTgL3/5C3Jzc/HBBx9g1apVePDBB71xCR7jrddEdXU1brzxRjz33HOeDtkrvFUOTz75JJYuXYrFixdjw4YNqKqqws033+y3fxsajQYTJkzA8uXLceDAAXzwwQf49ttvMXXq1Iue02q1YvTo0ZAkCd9//z1++ukn1NXVYfz48VAUxb0ABTXL6dOnBQDx448/uvZZLBYBQHz77bcNjn3rrbfEtddeK7777jsBQJw5c+aS5x48eLCYPXu2N8L2Cm+WxbXXXiueeOIJL0Tted4sByGEyMnJEcnJyaKwsFAAEEuXLvXwFXiGt8vhfD///LMAIA4ePOiJ0D2qLcvhP//5jzAYDMJut3sidI9ri7L44YcfWlR2bclb5VBeXi70er1YvHixa9/JkyeFRqMRq1at8vh1tJY75XC+BQsWiOTk5Ivev3r1aqHRaERFRYVrX1lZmQAg1qxZ41aMvlv14GNiYmLQq1cvfPTRR7BarXA4HFi0aBHi4+MxcOBA13F79uzBiy++iI8++qhZNTvFxcXYvHkz2rdvjyFDhiA+Ph7XXnstNmzY4M3LaRVvlUW9Tz/9FLGxsUhPT8fTTz+NyspKb1xGq3mzHKqrq3HPPffgb3/7GxISErx1CR7h7ddDPavVivfffx+pqalISUnx5CV4RFuVAwBUVFTAbDZDp/PN5SXbsix8mbfKYfv27bDb7Rg9erRrX2JiIvr06YONGzd65Vpao7nlcL6CggIsWbIE11577UXPa7PZIElSg4kaTSYTNBqN+5+hbqVOQe7EiRNi4MCBQpIkodVqRWJioti5c6fr/traWpGRkSE+/vhjIUTzvrls2rRJABDR0dHivffeEzt27BBPPvmkMBgM4sCBA16+opbzRlkIIcTbb78t1qxZI3Jzc8W///1v0blzZ3HDDTd48Upax1vl8Mgjj4gHH3zQdRs+XDMkhPfKQQgh/v73v4uwsDABQPTs2dMna4XqebMc6pWUlIiOHTuKWbNmeTh6z/J2WfhDzZAQ3imHTz/9VBgMhkb7R40aJR555BFPX4JHXK4c6k2cOFGEhIQIAGL8+PGipqbmoucsLi4WZrNZPPHEE8JqtYqqqioxffp0AcDtcgi8VNxNc+fObdRh98Jt27ZtEELgN7/5Ddq3b4/169djy5YtmDBhAm6++WYUFhYCAGbOnIlevXrh/vvvb/bz17drPvroo5gyZQoGDBiAN954A2lpaXjvvfe8cs0Xo3ZZAM7+QjfccAP69OmDiRMn4vPPP8e3336LHTt2eOOSm6R2OSxfvhzff/893nzzTS9dYfOoXQ717rvvPuzcuRPr1q1D9+7dcdddd6G2ttbTl3tRvlIOAGCxWDBu3Dj07t0bc+bM8eRlNosvlYWafLUchBBt2sfQk+VQ74033sCOHTuwbNkyHDp0CE899dRFnz8uLg7//e9/8b///Q/h4eGIjIxERUUFrrjiCmi1Wvcuxq3UKQCdPn1a7N2795JbTU2N+Pbbbxu1TQohRLdu3cS8efOEEEL069dPaDQaodVqhVarFRqNRgAQWq1W/OEPf2jy+Q8fPiwAuL4V1LvrrrvEvffe652Lvgi1y6IpiqI0ahv3NrXL4YknnnB9e6rfAAiNRiOuvfZab1++i9rl0BSbzSZCQ0PFv/71L49e66X4SjlYLBaRmZkprr/++kt+W/YmXykLIdStGVK7HOr7FZWVlTXYn5GR4dbfU2t5shyasn79egFAFBQUNCuW+tdCfHy8+NOf/uTWtfhmg3Mbio2NRWxs7GWPq66uBoBG7bkajcZVu/PFF180GA6/detW/PrXv8b69evRtWvXJs/buXNnJCYmYv/+/Q32HzhwAGPHjnXrWlpL7bJoyu7du2G329GhQ4dmP6a11C6HZ599Fg899FCDfX379sUbb7yB8ePHu3UtraF2OVyMEAI2m82tx7SGL5SDxWLBmDFjYDQasXz5cphMppZcSqv5Qln4ArXLYeDAgdDr9VizZg3uuusuAEBhYSF27dqFP/3pTy26ppbwZDk0RZxdOrU5f+/1cXz//fcoLi7GLbfcctnHXPhk1AynT58WMTEx4vbbbxc5OTli//794umnnxZ6vV7k5OQ0+ZimvrmcOHFCpKWlic2bN7v2vfHGG8JsNov//ve/Ii8vT8yePVuYTCaf7RvhrbI4ePCgeOGFF8TWrVvFkSNHxNdffy169uwpBgwYIBwOR1tcmlu8+Zq4EHy4z5C3yuHQoUPilVdeEdu2bRPHjh0TGzduFBMmTBDR0dGiqKioLS7NLd4qB4vFIgYPHiz69u0rDh48KAoLC12bL/5dCOHdv43CwkKxc+dO8c4777hGKO3cuVOUlpZ6+7Lc5s1ymDp1qkhOThbffvut2LFjhxg5cqTo16+fT74mmlMOX3/9tXjvvfdEbm6u6/0/PT1dDB061HWepsrhvffeE5s2bRIHDx4UH3/8sYiOjhZPPfWU2zEyGXLD1q1bxejRo0V0dLSIiIgQV199tVixYsVFj2/qRX3kyBEBQPzwww8Njp03b55ITk4WoaGhIjMzU6xfv95LV+EZ3iiL/Px8MXz4cBEdHS0MBoPo2rWrePzxx33yTa6eN18T5/PlZEgI75TDyZMnxdixY0X79u2FXq8XycnJ4t577xX79u3z8tW0nDfKof6YprYjR45494JawVt/G3PmzGmyLN5//33vXUwreKscampqxIwZM0R0dLQICQkRN998s8jPz/filbTO5crh+++/F5mZmSIyMlKYTCbRvXt38cwzz1y2HJ555hkRHx8v9Hq96N69u3jttdeEoihuxycJcbYeioiIiCgIBf1oMiIiIgpuTIaIiIgoqDEZIiIioqDGZIiIiIiCGpMhIiIiCmpMhoiIiCioMRkiIiKioMZkiIiIiIIakyEiIiIKakyGiIiIKKgxGSIiIqKgxmSIiIiIgtr/B2/NIGQ23zvtAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "troubleUs = [785,249]  #adjust this list by state if needed to investigate\n",
    "for u in troubleUs:\n",
    "    plotPoly(unitGeom[u],1.5)\n",
    "    for c in range(nCounties):\n",
    "        if u in countyUnitList[c]:\n",
    "            print(\"Here are the neighbors for unit\",u,\"in county\",c)\n",
    "            plotPoly(countyGeom[c],0.1)\n",
    "            plotCenter(\"county\"+str(c),countyGeom[c])\n",
    "    for uu in unitNbrs[u]:\n",
    "        plotPoly(unitGeom[uu],0.5)\n",
    "        plotCenter(uu,unitGeom[uu])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8378940d-4387-4188-8ae1-a62010500ecb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now build the border topology\n",
      "counties and clusters done, now working on (frag) vtd-based units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Now build the border topology\")\n",
    "borderUnits, borderType = list(), list()\n",
    "for c in borderCounties:\n",
    "    if c in unitCounties:\n",
    "        borderUnits.append(allUnits.index(c+0.5))\n",
    "        borderType.append(\"county\")\n",
    "for i,L in enumerate(CCBlist):\n",
    "    if CCBgeom[i].intersects(MAP.exterior):  #should always be the case\n",
    "        borderUnits.append(allUnits.index(i+0.25))\n",
    "        borderType.append(\"cluster\")\n",
    "print(\"counties and clusters done, now working on (frag) vtd-based units\")\n",
    "for c in borderCounties:\n",
    "    if c not in unitCounties + allFusedCounties:\n",
    "        for u in countyUnitList[c]:\n",
    "            if unitGeom[u].intersects(MAP.exterior):\n",
    "                borderUnits.append(u)\n",
    "                borderType.append(\"vtd\")\n",
    "    \n",
    "for i,b in enumerate(borderUnits):\n",
    "    plotPoly(unitGeom[b])\n",
    "    if borderType[i] == \"cluster\":\n",
    "        j = int(allUnits[b])\n",
    "        for c in CCBlist[j]:\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "            plotCenter(\"fused\",countyGeom[c], 6)\n",
    "for c in unitCounties:\n",
    "    plotPoly(countyGeom[c],0.4)\n",
    "    plotCenter(\"unit\",countyGeom[c],8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "937d923d-9e95-45e9-9aef-77cb4377781d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking that the list of borderUnits is contiguous all around\n",
      "number of border units = 148\n"
     ]
    }
   ],
   "source": [
    "print(\"checking that the list of borderUnits is contiguous all around\")\n",
    "print(\"number of border units =\",len(borderUnits))\n",
    "for u in borderUnits:\n",
    "    isUnbroken, smallPieceList = isContiguous(list(set(borderUnits).difference({u})),unitNbrs)\n",
    "    if not isUnbroken:\n",
    "        print(\"border is discontig if we skip\",u)\n",
    "        for uu in smallPieceList:\n",
    "            plotPoly(unitGeom[uu],0.5)\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotPoly(countyGeom[29],0.2)\n",
    "        plotPoly(countyGeom[55], 0.2)\n",
    "        plt.show()\n",
    "borderSet = set(borderUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "93603f77-77fa-4453-b54b-5ea07fcb6721",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here, we identify 'border' units that aren't needed to define the map border\n",
      "Bueno! no 'border units' that could be eliminated from the border set\n"
     ]
    }
   ],
   "source": [
    "print(\"here, we identify 'border' units that aren't needed to define the map border\")\n",
    "canBeRemoved, powerNeighbors = set(), set()\n",
    "for u in borderUnits:\n",
    "    uNeighbors = list(borderSet.intersection(set(unitNbrs[u])) )\n",
    "    if len(uNeighbors) < 2:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        uu = uNeighbors[0]\n",
    "        otherBorderNeighbors = set(unitNbrs[uu]).intersection(borderSet).difference({u})\n",
    "        if len(otherBorderNeighbors) > 1:  #this neighbor of our 1-border-neighbor border unit makes a border chain without the problem border unit\n",
    "            canBeRemoved.add(u)\n",
    "            powerNeighbors.add(uu)\n",
    "        plotCenter(uu,unitGeom[uu],6)\n",
    "        plotPoly(unitGeom[uu],0.5)\n",
    "        for uuu in set(unitNbrs[uu]).intersection(borderSet).difference({u}):\n",
    "            plotPoly(unitGeom[uuu])\n",
    "            plotCenter(str(uuu)+\"=N of\"+str(uu),unitGeom[uuu])\n",
    "        for uuu in set(unitNbrs[u]).difference(borderSet):\n",
    "                plotCenter(uuu+0.1,unitGeom[uuu],6)\n",
    "                plotPoly(unitGeom[uuu],0.1)\n",
    "        c = countyNo[allUnits[u]]\n",
    "        #plotPoly(countyGeom[c] )\n",
    "        #plotCenter(c, countyGeom[c])\n",
    "        plt.show()\n",
    "if len(canBeRemoved) == 0:\n",
    "    print(\"Bueno! no 'border units' that could be eliminated from the border set\")\n",
    "else:\n",
    "    print(canBeRemoved,\"is the list of 1-neighbor 'border' units that can be eliminated from the border set\")\n",
    "    print(\"... as they are enveloped on the border; the enveloping border unit has two other map-border neighbors\")\n",
    "    yesRemove = input(\"enter 1 to remove these from the list of border units\")\n",
    "    if int(yesRemove) == 1:\n",
    "        canRemove = True\n",
    "        for uu in powerNeighbors:\n",
    "            testSet = (borderSet.difference(canBeRemoved)).difference({uu})\n",
    "            if not isContiguous(list(testSet),unitNbrs):\n",
    "                canRemove = False\n",
    "                print(\"We can't complete the border if unit\",uu,\"is not included in the ring\")\n",
    "        if canRemove:\n",
    "            borderSet = borderSet.difference(canBeRemoved)\n",
    "            borderList = list(borderSet)\n",
    "            borderUnits = list(borderSet)\n",
    "            print(\"Success! we removed\",canBeRemoved,\"from the list of borderUnits\")\n",
    "    else:\n",
    "        print(\"Fine, be that way.  Sheesh, I will keep them.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b13c68e3-33ed-48b3-acca-78012ce0bf61",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is the final border set\n"
     ]
    },
    {
     "data": {
      "image/png": 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QFh2DbLVhiI/D1Lt3J38bgiCcDCJg6WE0VcW1cydh869t3PbjYAXAkjaY5PfeJRlY/sLvGfqfZaR+54LftH9+nawjyBhEkLFpBMx23/dYrE2jdi6LDuXfJRXYZZkSSUP2KUQqEkN0euJVA0l2M/33rMOt2kkYMa1T70uxRWOuy2i3TExSOFBOwaHyLgUsIeGJxAxcQ9YPYUyaXYPF1nqnY31kJKpTQ/P5kAzt90uJmTwe6+dvs/+HdNKmje1UPaYmjWFL7bt8e3AH5/Yb1en6N74Pu4+y2q7/SeYeyGPL7hxGD+1N+g87WbG7kI3WBAD8KuhbyVZNv/Qc9vznRdZ8tIJzf3Vxm+dWvQpKJ5ccG3vpXNb99yX+9/TjzPvdAy3263Q6LBYLFoul3fPkq2UMOmdkp15TU1Q0j0LBgyswDx5G4jO3o7rdqA0uVFcDmsuF2uBi5YoQAM64sh9+r4Lfp7J28Vso7nzC4qYi62V8bjceVwPO6ip8bhc+jxufx4O3oQGfx43RrzBjz+FA9lGSSHr7Layjuv57FgTh+IiApYfxHs5BranBMmx4p485b8Hj5O2qg+OYEE3TNLxuP0Zz00fh4UGJPDwosd3j8nbboQgsIZ2c8yU4HnthLZqqtjlxW1xSBJBBeWF1J2vfZMLFw/hkUSnpq79i0qy5rZYxRsbgBTxlBZjjkts936AZEzgg6yj4bl2nA5bZA8bz5E4dX2VvOK6AJSpYZn+useOCx9i+fheXfnIIVdbBhu0AWJVQ5tvKmDQsid0bJfStZDtCEyMYGt2f9JzdnFExA2t4679Hza+h0nEHX4CJ511I7r69FGz8njVLPuGM2Zd0+n34fD4ADJKej7/7Ak+dmxEXTezwOEknI1llQEEyWTD17dtquZDVS6j22Uk7I5BJ3LVqG4p7KwmDZnLBwoUdvs7b506nJNhG6vdrwOMh/9Zbybn+BnhoHI6J04mLuwxZNnV4HkEQjp9o0O1hXDt2gCRhGTa0S8dpXi/+sjI8hzrX5HGU4ldRFQ2jpePYte7wQUr+PJ2KP08grvg16gytXxxaow9NRI9CQ0V+m2XMVhOa3kt1aef6cRwrttdAHDEFbP88gpyM7a2WMUQHsg7uooMdns9qt1IQk4Jv+7ZO1yHMasesJrGzvPPHHKtXmBWvx47X37kAAeDRj7eR4KnmoV4enh4s8c3V/Ul/+Fz+/Mj1nHPpdPyShMHQehA09bKzUFBZ+/HKNs+v+RVUOj/U/PLb78Yck8Cm9/7LoX1tr4P1Y15voNP3ubPOIzk0ns+2fc2X//4YtdM/CxXUtnuK94r1Y/DVNz7/7q1/YbBEctHvOkhJEgjqa82Bn2F55n6MCQkkf/ABITdcRWHESjIyH2Ljpln4/fUdnEkQhBMhApYexrV9O8Y+KegcnZ+tFsDYuzfu3bsp/uOfunSc1xW4IBjNHXdybVjyCCH+/dRLcRTHXk/QbR93+nX8B9cEzlF8oN1yequGs9LbbpnWSJKOafNGA7D1q32tljFHJwPgKcnp1Dm9g4YSeWgfqtr5C3Zvexplvv2BZQC6qH9UKCCzp6SoU+U//ug7tujCuX2QjV//9lIuufZ8UoekYrZbG8sosozB2HrzlyMimHBrCFvzd+H3+Fp/Eb+GJnX+/ev0eq7502NIRhMf/eUB1i79rFPHHc2w2Ow25t1+PZNSR7OheCfvPPkqrk6M7ELTIVvaznDYQ8z49BZqsgsB8LpqiOs3HLO1/eYpCMwGPe3iKwD4+OlFAMhmM/arZgXqXBOPy5VPfsHbHddTEITjJgKWHsa1YweWYW13HG1LzEMPEnTeeaDr2pBQnycwjNhgbjvDorjdFD3zK6IrP0WT9PR66CPib/kHptCwTr+OvXw7ANao5HbLmYNkvJ24PrUmNDImcA576x9rc1RvNEnDW9J2ludYERPGEuKuY/Ltb/Lh1vxOBSET4kej6WpIL8zufMWPGBobmPRue2HHI5ly9x/i3g3VDG4o4ZLrL2i1jKaqgYDF1PJCnp+fzxtvvEGpqxI/KlUfZ9Kwswztx1kKRUPtQsACEBwWzsW/fwhJ8bPh4/91KuBrbBIyGJBlmbOuuYDLps8m113CK8+9RHVheZvHqj4/GOzoQsxtlul/yVgkTWXPBxsB0NR6vO7OZ0QGXf8rxvQfikcnU5ebg6Zp7Pt2AQAJoVcQHTWL7OzH+XZlHzZsPPe4AlZBENonApYeRK2vx5OZiWX48OM73u1GNrf9T7s1HWZYfG50f4smtvojqtVelKTe3fbJ6sshYzns+hC2/hfWvwDfPQ4r/kikv4hCc39s0a2vjXSUI9yM6tYd1z98R2goIb3yyN1uI2vHuhb7jdZIVBv4Sks6db5h505BRSKt4hD3fLCDr/a0Pywb4JKBkwD4POOHLtX9QNUBviv+FFB4elk5059cTVV925mmA5l5+HR6/n7NWHStBCQAqsuFotdjMDbfv3fvXv7zn/9QWlrKjPHTuKL/uaglbirf3U/l+z/qGK2A1tl1Go7RZ/AQxlz9a6T6Wpa+9nKH5Y8GLMcu2TBk6kh+dfX1eDQv7776Nu7a1psKXdv2Isk6/EWHcO3YgXvfPtSG5mWtsRFES8Xszjay8tH3ASjKWMPzv/4tP3zwbafeU6/xgT411QcPovjd1MUFsjUJ0xYQFja5sVx9fRYeT+eyZIIgdJ7odNuDuHbvAVU9rgwLgOZ2IQd3bVp+rzuQYTG2lWHx1DU+NNy0lKTEJBSfH3dVNbaoI/OvFG6DvUtgw4vgdzcda7SD0QZGG3XWXhxSE2h74vyAsBg7xTsUqitqCY3o2nsBOPdX0/j8xR/4+t8m/L9ax8Axx3Tc9KtIfglVaqP540fs4aEY+/djiiufrxnLw0v2Mik1Akcbc58A9A6LQuePZUvxVuC6Ttd7ccZiPsv+hLT+FuIN4/lqdzX3f7yLEb1CuPmMlBaT8w0c0R/5h02s+n4PaWPTWj2nrz6QQTCYm4KA/Px8Pv74YxITE7nmmmuaBQj1m4qp+jgL95gYzH1CAhsVDe04v9acMfsS0r/4hMKMjvuyHJthOVZs30SumHMFb334Lq/+82XOnDqdgVOa/31oBN5D3TdfUPPRvxq3mwYOJOjcc7FPn4apd29sOjfFkoV9+Rbi4yejj7RTcmAPGz58jujesaSOHtRuHbd/tRSAhqJCdPqmfkHO0r3ExFyMzZYKaGzecgl79t7NyBHviiUQBOEkEhmWHsS1fTuyzYapT5/jOl51uZHNHbfJH8vrPpJhaavTrT2Ssmlvo2kSDW/+moJvv6Jm0UQsL/Sl4JPX4PAP8N8LYe1TENEXzvkrPFAIf6yCBwrgnky4fRsZZ77OCvdQ/P72Z7KNTgw0M+UfLO3S+zgqPCaJSxeehTmolu/eLqOuqqkpoWrTl8hucEyc0unzOcaMYUJDPv+7eTzFtW6251V3eEyceSAF7tb70bQl2hqNxWBh6Q138+ScsVgMOr7cU8yi5fvJKnXS4G3+c4tNjOZ8qYz/FEg0VNe2ek5PbSDYNByTddu4cSPBwcHMnz+/xQKU1lFRGJODKP/PLuq+P9JspnLcAQuA3mLD7+64E/XRz8WP6wTQKy2Fqy+4nDJ/Nf/79hMOr9/fbL/OEQicQ6+5jNjHHiXp7beIXbQIY69elD3zDIdmX8T+IUMx7l0PQJCvlEse/C1z7r+NafNvBvzk72u/bxUE5jACcISFI0k6hof8E32FzOaNF1OXl05Q0BAcjiEAVFdvorpmS4fnFASh80SGpQcJ9F8ZitTFfihHqW43sqWrTUJH+7C0/ZqR0y6k2PcaEWtvRv/9XLyaBVlSCdv7JOytgvhRMPdNsLbdpyX4SOanvLycmJiYNsvFp0QChyjNr4LOjSZuISgskrGXGlnzuo0NX6zhrGsuBcDvqQFAZ7C3eaxaX49r9x5cO3YEblu3olRXMzLaQkyQmS92FjGlb2S7rz86ehSfFKwkp6qUpNCO55N5Nv1Z3s94H6fPiV/14zAb2PbHszhQ6uSC59Zy9tNrGJ0Uyoe3Nh/mOzzCxNJyG742Wmx8DUczLE1BrCRJ2Gy2FpkMCAwRjrxxCDVf51DzxSE0RUNSJbSOl1Jqk6oo6OWO/80cDViMxtZHNCWP7s8d8f/HCy+9yMdffsY1IXaiBgZGfSm1XpAgcsFNSEdWu7aOHk3IJRfjOXgIX1Eh3uxscnfIWCUdVz52ORs+zWbnqi343VtA0jNx7jktXlPTNEoPZROdkkrOzu0cLg0085T+4ymixo4jfOQsRjni2LxvLgdW3M/IX39N5YGmIEVfHwohUFnvZU9hDZNTI0TGRRBOgMiw9BCapuHasQPzcTYHAWguF1IXMyw+j9I4+2d7Ys66FNdln1IQdzO6BwLDgi2+Aug9Fea9326wAjQGKdXV1W3XX9PYl7kbVfJTX9X1NYWOldx/HKbgcg5sMONxB9Yaipg8B9UKFcv+h6Zp+IqLqVu5krJ/PkfmlClkTTmDjDFjyb3uOipeegnN1UDIVVfS6403MNiszJ+YxEfp+dS42m9SuqBfILD4dF/n+rF8n/89SUFJvHHuG40Xd7NBx6DYIB6/bCgXDI0lu6ypJ3J1RTW33/8azxXo6esuJzg0qNXzeo80CRmPGQljsVhwuVxt1kXSywSfl4xjRi9qvzxMiBJxQv8lNMWP3ImZgo8GLLp2gvXQ2HBuveUW0Ml88L8PcK4vDExsV+tFdhgbg5VjmVJ6Y580ibD586mPGUiDU+H1e9eyc1UggzRwYgpofuRW5gbK2vgDb9+/kIz13/Pp438GYOYV12GtraNg4Z1oXi/mkATC/COoCT2I6vdxKON59O5wEg7dyfefVHH9lgOM/MsKrn11E5+v3QaiM64gHDeRYekhfAUFKBUVx91/BY6v021hZhWaqpGfUYXVYcTiMGK2G5Bb+efvGDoZx9BA58JihiPZwom+4m1oZ6Xmo+x2O3q9vs2AxeVy8dlnn7F//37igyZjM4R06X20qGtIFEOmO9jyqYm8zD0kDexHXf1u/KMcSEt2kPX1JJSqKgAkgwHN58M2cQIRt/8flmHDMPXp0yLTZZBl/KqGx6eApe20w5jEPuAPYV3BZu6g/cnTqtxV6GU9oeZQhkcNb7ZPliXmjklEJ0ss3VmE26dg0kk8dOvjfJ4yCUzw3uVD2jx3Y8ByzMyyDQ0NmDv4jEiSRPBZSRhibGS9swqXw91u+faoih+5E5+PjgKWQ4cO4XA4+HrlCiS7Ho/LT/WSbGq/yUE269EFdTzhXni8neKDNQyfkUi/sTEER1rI2b2dXd9CfVUFwVGBoNrtdFJ6OJusTYEmpKXPPo41KJjL7/krkUEh5EW+ScPGjRy6bA6e7Gxs102gfIxG3q5XqbGuJaz8bN6Nnc5LFh+6bYUYgGHSAWZ/Ow+yp8D1Szv50xME4VgiYOkhXNu2A5xQwKK5XEhdbBLK2hLoK7Lkme2N2yQJzHYDBpOOcRel0G9MyyaczAGvcGhHGdd14mIUOKdEcHAwGzduJCgoiEGDmjo45uXl8eGHH+LxeLjiiivI+d5PXYWnS+/jWF63n/K8OvwNiUApG5Z/QU55YB4N0xkWQpUoQkdejnnQQMwDB6KPiekwVb/5cCVPrcjkitGJRAV1fMGPNAzgoHN3h3X9zYrfsL9yP9cOurbNMrFHhusW17hJNCqsThhOgrOM5QvPQMvcT8miJdinT8M2fnyz43xHMikGe1MTWGVlJRERHS9WCWAdEsE23yqSQod3qnxrNEVB18bEdceqrAwsotRawJKbm8sbb7zRbFt0dDQxt4zGua6Qhu1lWFJCOnyNqfP6M3Ve/2bbgqOiAfj6pecYdcHF1JaW8sP7b+N21iFJgaxLbN/+XHDHvfi//JqDTz+NeiQQ9JWWgqri/e9a9IlwgCcAkFUrr5i9gIQhuw4ZlccMr9EgSVgOf9/mwqKCILRPBCw9hGvHDoxJSehDQ4/7HIEMS9eahH77r+l4Gvx4Gvy46rw01Hpx1QVuG5ccIv3LHPqOjm52Qc/bV0nm5mI89X40VWs1Fd+amTNnsnz5cnbt2sWgQYNQVZV169bx7bffkpCQwGWXXUZISAhV+w5QmtO5TreKolJZUE/J4VpKD9dScriWqqJ6NA30RpnwRB9Jo0NIHvQPghxpWK29ka7tWh+h/cW1XP/aJoYnhvCnCwd36pgh4cP5tuxlatxOgs2t95n5NudbKt2VzEqZxe/G/K7Nc8UGB36na7LKUFUNj87IOYdXkX/B3wMF9Hp8hYUtAhbvkYDFaGta3LC2tpY+XejUrfh86NroV9IZgYCl404wxcWBIeNffvklkydPxnFk4sS6ujo++OCDZmVvvvlmdDod+nALIRf2IfiClqOoOis0Jo5Zt/+OzZ9/zCd/ewQkibRpMxl21vk4wiOwhQT+Ht0ZGRQ8+mjjceZhQ0l47jmUqiqq3/8A7R9vE/HqX6AsnsL9btRoqbH551+GZ1iQ7KNSl8jVai/m3bmQutQiHLPPpVfiDcdVb0E4HYmApYdw7diBpZ2VhjuiaRqax4Nk7tp6JpIkYbYZMNsMBEc2D3ZMVgNrFmey8o19BEVayN9fxYRL+7D0uR3IOonQWBsadPob48CBA9myZQv79u1j+fLllJeXk52dzeTJk5k+fXrjt+vgSAvOSjeKX0Wnb+pboKkaNWWuxuCkNKeWsjwnik9FkiXC423E9Alm2JmJRCUHERZrRdbJQMsOlZ3lV1QWvJNOYpiV/1w3Goux/WDH41dYsbeEg3lRSBaVr7M3c/ng6S3LKR4Wrl5IkDGIfqH92j1nbLAZWYI/frYHo05moB2mTRpIwsLLsAwfTtk/n8O1e1eL43yuQFOO6ZgMi9FobBxC3Bl+nxd9JzIkbVIUKquref3119E0Db1e33gzGAzNHns8Hnbs2MHWrVsZPXo0kydPZsOGDfh8Pu68806efvpp4uLiiItrPjj+RDuyDpg0lf4Tz6AkOwu90UhEr+QWZRq2NHWmDZp9IbF/+QuyyYQhKoqYhx4k+r57kQwGfGUNPJm3+2jF6D2glnMOb+F5JZY6TWbKvw5S7zyId7BK1sAtWC3JRES0/HwIgtCSCFh6AG9lJe5du3Dv2kVhaRGy1YbObkdntxOUkEhYn1RkqxXZZmt2k0ymxn/WmifQhNLVPiztGTItgcM7y9m/oWnCtI/+vhWA+X+diC2k64u9jR8/nuzsbDZu3EhISAjXXHMNqampzcoERVrQNCg5VIu73tcYnJTm1OFpCPR1CI60EJUcROqoaKKSg4hItGPoIJg4HpklTrLL6nnp2lHYTG3/uewrquV/m/P4dHsB1Q0+RibFYpbtlPsygOYXJFVT+frw1wDcNuI2rhpwVbt1MBt0vH3jOEx6HWnxQZh+tPqyISGB6vffp/LNt5qt8u3zBAIWwzF9WIKCghqbXzqjvrrqhAICVfHj83rR6/XYbDb8fj9+vx+3243T6cTn8+H3+zGbzSQnJ3PJJZewYcMG1q9fz5YtW9Dr9QwYMIDg4GD+8Ic/oCidX2epKyRJIia17cDRXx4YHh95912E33hji5/J0dW/DZFWBg+LhtoqbusVxfA+obhXz+Af43/FPR/fR9iRvtOaKZB9yTrwV/R6B8HBo8ipaGDDwQpmDIwm0iEWUhSEHxMBSw9Qsrepr4O8Yxc6nx+doqBTNeqBNicQ1+kCwYvVinzkoiSdyLfhVpx9Uxo7V+ZhNOuxOAyseG0vQ6bGYw0+vtfp27cv99xzD4qiNA51/rGjmZ5P/pEOgCXISHRyEMNnJhKVFERUUhBm+wmMte2C1Cg7DrOevYW1nDO4eV+eGpePz3cU8v6WPHbm1xBhNzJ3dCJzRyeQGuVgwbcj2V7WciHE4vpiHlj7AMlByQyJaLvT7LEm9mm734l96hmUPfUUVYsXNwYsqsdDQVngIpubm0tQUBBms5nIyEi2bduGoijtjsg5SlNV1n3wDomDh5AwsPUJ6tpSnpeDQZIYPm48Z13bdh+dH5s2bRrjxo1rDFqGDAn8jAwGQ6vDsU+FsPnzccyciWVwx02C149K4nqSmjbM/5h+wCO5nwFrATjUJ4kQDqIoDWxNv4Jiz3j+8N08ABbOdLNwZvtZN0E4HYmApQcoralk7eiB3Pbf99Hpm34lnz+1CHd5ORff8XvU+vqWt4aGZs+1iROxjh1zUutmsugZM6t34/N+Y9ueQ6Wz7Pa250EBcISZmX7tAEwWPVHJQdhDTd02f4VRLzNjQBRf7SnmzrP6oWkaGw5W8v6WPJbtKsKnqEzvH8W/rxnFjIFRGHRNTVgjo0by8s6X8av+ZnOROIyB/hkLRiwgLaJrQUBrzP37E3nnnZS/+CJ5t9yKNzcXb04O1aNHQe/eLF68uMUx+fn5JCUltXK25nQGA4rPxyd/f4TrnnyRoIj256CBwNwrW5Z+wrr33yY4Opax57a+1lF7LBYLZ555JmeeeWaXj/0p6ENDT6h/GcCSiFjmAt+dPwtPsJ8QDuLxBLKXbk8Rf7loMJ9uL+SZb7I4Ly2W3MoGxvYOI7idEWmCcDoRAUsPUJSVQVTvPs2CFYDKwnziBwzGEB3dTTXrHpIkMWhSR5P4nzrnDI7h0+2F5FU28Nt30tlVUENyuJXbZ/RlzqgEotsYNTQyeiQN/gYyqzIZFN40KspusGOQDVS4Kk5aHe1Tz8CVHshI2SZMIOy665jbty9qUi88gMfjwe1243a7KS0tJTKy48ADYOHbn+BpqOe/d93KN688zyX3Pdxh8PjpE3/h0LYtjJl9GRMvvxr9CXTa/SUZOmIE/5NlBgwYQEZGBqNHL+NA9iKqq75Hbx7CtROS8fhVtuZUcc4zgdXNrx2fxF8uPvGgVhB+CU5o4rhFixYhSRILFy4EAuuB3HvvvQwZMgSbzUZcXBzz58+nsLCww3NVV1ezYMECYmNjMZvNDBw4kGXLlp1I9X42ig5kEtu3eQpYVRQqCwsIT0jsploJRw2KC0zMdqi8ngOlTn5zRgqr7pnGgumpbQYrAIPDB2OUjaSXBAKJclc53+V9x4s7XkSWZCrdne9L0hHzgAEkvvRvEv/9L2IeepDQK+ZiHTkCe3g44eHhxMXFkZKSwqBBg5g2bRpWq7XT5zZZbcy86TYObd/Kvu9XtVmuoaaaT594lEPbthCdksoZV98ggpVjzJgxA4D9+/ejaRp/XlrN3zfMZ2XeVCYMDaz8bAlr/nnqqJO3IJxOjjvDsnnzZl5++WWGDh3auK2hoYH09HQeeughhg0bRlVVFQsXLmT27Nls2dL2uhper5ezzjqLqKgoPvzwQxISEsjLy2sc2vhL1lBTTW1ZCTGpzeeHqCouRFX8hCf06qaaCUd9tDUwK2rvCBthNiMGndypJiqjzkhaRBqv736d1/e8TmlDYKh2qCmU0TGjmZow9Set98nUZ9RYBkyayvIXnqKquIhJc69u3Oeud3J4+1a++GdgHpKEQWmc99u7uquqPZbD4WDw4MGByROtoTy2M9DHKHbsHfx5YwOyvIMNewOfEUOwEV+NF90p6qslCD8HxxWwOJ1Orr76al555RUePWZuguDgYFasWNGs7HPPPcfYsWPJzc2lV6/WL76vvfYalZWVrFu3rrFTXWfa138Jig5kAhD7o4ClMj8PQAQs3WxfUS3/XBlYGO/uD3ZQWuemzt35YcFz+89lSfYSBoYNZHDEYNLC04ixdTxRXU905g2/Yf8P37Hho/eYNPdqNFVl57df8c1/XgAgJrUfk6+cT9KQ4d1b0R5KkiQuv/xyIDANgbT2EI9+sY/PNuU3K/fMr0dzVnIEj777AveunAe9voDkyd1RZUHoUY4rYFmwYAGzZs1i5syZzQKW1tTU1CBJEiEhIW2WWbJkCRMmTGDBggV89tlnREZGMm/ePO699942RzJ4PB48nqbZUGtrW1+xtqcrPpCBNTiEoMjmi+SV5+dgtjuwBod0T8UEAErrAp+xgbFBRAeZuXVqHy4eEd/p42elzGJWyqyfqnqnlMURxIQ5V7H+w/coPXyQ1W/+h7w9O+kzejz9J05hwMQzfpaBWHeQJIkbp6QQEWfnti/2oEaZifbB1f1juLhvNJri47ayVwOFP1sAt28PTEEtCKexLgcsixcvJj09nc2bN3dY1u12c9999zFv3jyCglpfoA3g4MGDrFy5kquvvpply5aRlZXFggUL8Pv9/PGPf2z1mEWLFvHII490tfo9TtGBTGJS+7X4R1+Rn0d4Qi9xAehmU/tFcvhvv4yA42RIGjKC9R++x7sP3o01KIQ5Dz4qMion4OI+UZyzIIJ7MvL4qKQKT7gJRdPY8OkfGOfMI2v4zfTd/jLs+xxt4IVsK93GkMgh+JwN7Ph6GaMuuBhjF2e3FoSfqy4FLHl5edxxxx18/fXXHS6g5vP5uPLKK1FVlRdffLHdsqqqEhUVxcsvv4xOp2PUqFEUFhbyxBNPtBmw3H///dx1V1M7eW1tLYmJP68OqpqqUnwgk9EXXtpiX2V+Lma7g+riIkJiYruhdoLQUlz/gSQNHYEjPIJp82/EZLV1fJDQLotO5vmBvUizW/hLdiHbCw+yeNcrbBi+gEkXPgzOQ/jfn88/J8zj9eLvuW34bdjfz6D4QCa20DCGzjj+mZwF4eekSwHL1q1bKS0tZdSoUY3bFEVhzZo1PP/883g8HnQ6HT6fj7lz53Lo0CFWrlzZbnYFIDY2FoPB0Kz5Z+DAgRQXF+P1ejG2MtLAZDJhMv28Z4OsLCrA01Df6gybEUm92ff9Kl694ybm/OFRkoYOP/UVFIQfkSSJOX/4S3dX4xdHkiRu7RXFQLuZj756ER0qqa4CNFlH/pn38XxNOsuKvwdgiJTC+gOfA4FO+4JwuuhSwDJjxgx27Wq+ZskNN9zAgAEDGvubHA1WsrKyWLVqFeHh4R2ed9KkSbz77ruoqoosB0ZaZ2ZmEhsb22qw8ktRfKTDbUyfvi32nX/b3cT1G8i3r75IcPSJT9YmCELPNy0siD5nXERN1t+Z6d0CbwZGYepsVvSSjF9Tyd6wHmtwCA011djDOv7/Kgi/FF2ah8XhcJCWltbsZrPZCA8PJy0tDb/fz5w5c9iyZQvvvPMOiqJQXFzcmCk5av78+dx///2Nz2+99VYqKiq44447yMzM5IsvvuCvf/0rCxYsOHnvtAcqysogLC4Bs631mV8Pbt1IXL+BhIiARRBOG4nxA3H8Pqfx+UBrLF/Pep9/THsagMPbtpI8bCQA9tCwbqmjIHSHE5o47sfy8/NZsmQJ+fn5DB8+nNjY2MbbunXrGsvl5uZSVFTU+DwxMZGvv/6azZs3M3ToUG6//XbuuOMO7rvvvpNZvR6n6EAGsX37t7qvvrqKwzu3MegMsZKrIJxuZHMQO+fv5PYRt+PKruHfTz3Cl/uXkmCMwVLpp1daYGV3qzGIqo+zcB+o6uYaC8JP74Sn5l+9enXj4+TkZDRN69IxR02YMIENGzacaHV+NnxeD+W5hxlyZusd5vb/sAZJkuk3YcoprpkgCD2BJEncNPQm6hZ9jqbWoxbtxTHET0yfNCISA/NUed8owgs07Cwn/uEJzY4vz68jLM6OLIuRhsIvw0nNsAidV3roIKqiENvGkvb71q4iZeRoLPZf/my/giC0zllViaaqpJ41HYdLz7hNNkqys1jz6mukhTd9mdHcflSvAoCiNPDmH5fxv0c3881re5p/iaw8BJ34UikIPZEIWLpJ8YEM9AYjEb2SW+yryM+j5OABBk3pGSvVCoLQPV66ZT4AwcZgrEEhjdu9+XUMDpoIgDogMDt41QeZOCsOsvq7IeiCAk3wWVtK+fo/eyjIqMKXsQr+ORy2v3NK34MgnCwiYOkmRVkZRKWktlihGQLZFZPNRu+RY7qhZoIg9BQ3v/hfADI2rOX6p5rms0ruHejD0hDhIv6KUcjRBg7oHmLjjrMC+4cFOvKPODeEvP2VfPr0Nv7zjJ8M19TAzLlb/wsHvzul70UQTpQIWLpJ0YHMVpuDNFVl7/er6D9+CnqDWPhMEE5njvAIxsy+DGdFOW/ffycAZ998B0nu/pj6hdLvnrNRyt00NGThjNoGwID+j5Lc5zIAwpPq+dUTU7jiwbGgaTSoIYETf34HvDkbnGXd8bYE4biIgKUbNK3Q3DJgyd+/h7ryMgaK0UGCIACx/QYAEBwZxbxH/8HgyYGmYs+RkUHVXxzE5ExoLB8aOhFHSAQADbW1yLKEwwEqeuxpE+DCZ5tOfuCbU/QuBOHEnfAoIaHrig5kAC1XaAbY9/0qgiKjie838FRXSxCEHih19Hhuf/NDDKam5VBCLkml+pMDVH12AO/hWozxIcRtu53iIS9z8OBTmAx9gUH4PCoAdQWFADh694ORZwYyLADxI0/12xGE4yYClm5QlJXZ6grNfq+XzA0/MOLcC5BkkfwSBCEwvPnYYAXANjaGhu1l1K8PzGdlTg0hLHMK+JwUlr6G6nUAT7Hr6yD2r15HTKwfAEdsJCi+phNFtj4PlCD0RCJg6QZFBzJaXaE5e+smPA31DJwimoMEQWibJEmEXdmf2hU5BJ/XG53NQPB5vYlQBmDODSI66iIyPz2A26ngRqGuEiQUrNExoDfCnNfhwxtg/zIYcH53vx1B6BTxNf4UO7pCc6vNQWtXEdOnL2FxCa0cKQiC0EQfbCJsTj90tqbO+Tqdld69/w+rrVfjtkvvCTT7aEhIKx+BjOWgBZqKWHwVm19eQJ3bhyD0dCLDcopVFhXgdTW0CFgaams4tG0LU6+9sZtqJgjCL8lFd47A6jASGmtl6JkJpHo/hO1vB27HsOZ/z5Uvb+CzBZPQ68R3WKHnEgHLKVZ8IBMkiZjU5is0Z6z/Hk3TGDDpjG6qmSAIvyQJ/UMbH0+Z2w+0+yHvTHCWQvwoeHoQAFlBE9hTWMv7W/KZMTCKnfk1zBgQJab0F3ocEbCcYkdXaDZZbc227/t+Fb2Hj8IaFNxNNRME4RdNkqDX+MBjTaNODsah1pDszeTi4XE88Mku+CSwe1iMmU9un44sOv8LPYj4NJ5iRQcyWjQHVRUVUJSVITrbCoJwakgS97mvY7kyBu+AS3hkdhpn9o8kwhr4Druj2M1br72O3yf6tgg9hwhYTqGjKzT/eMK4fWtXY7RY6DN6XDfVTBCE0834C3/Nrb47ea5qHP/bnENx3mHKGwLDn0eYSjiUn8dzv70Bt9PZzTUVhAARsJxCpQezW12hOWvTelLHTMBgNHVTzQRBON1cOjKBP14wCEmS+PvyfexvsHBZootnL+rNVcHlALhMNt74w1xK83d2c20FQQQsp1RRKys0l+cepjz3MLF9B3RfxQRBOO3YTHp+Nbk3b/5qLOkPzuTaoP04ynYTI9dRrVcJCi5BHy6RelEGCzOXc8WWzRxucHd3tYXTmOh0ewoVHchssULzN68GVmBtbSFEQRCEUyHYbuH23/yKF154gW+++ZjxEz5stn+ldA7Uwf8eWI/drXH2jYPpPSwCvUHXTTUWTkciYDmFig9k0HfsxGbbakpLGHn+RUSnpHZTrQRBECA8PJw777yTzKzfUl0NmmJD0tUDEKsVUCNH4DFI2N0aX/9nDwaDTNLQcKJ7BzN8Zq/2Ty4IJ4FoEjpF6qurqC0rJbZv0wghn9uNs7KCqOSUbqyZIAhCgMPhYNDAxwAagxWAy1hMmF7mowl2APoMCeM8q0z4/ko2fn6oW+oqnH5EhuUUKc7OBJqv0FxVHFhBNTQ2vlvqJAiC8GMWSy9ioi+muORT7PYBREaczfTkBTwg6XjiYBEhWRqpObVIskSMQWJAv0r8Xi96o7G7qy78womA5RQ5ukKzIyKycVtVUQEAoXEiYBEEoecYPPgfDBr0ZIsFWn+fEovvWgel/9wOQE7NDras/JLa+r1ceNf93VBT4XQimoROkaIDGcT27d/sH0BVYQFmRxAWu6MbayYIgtDSj4MVgKK/b6boz581Pt9atRKAzI0/8PnTf6OhtuaU1U84/YiA5RQ4ukJzTJ/mI4GqigoIE81BgiD8DKz76ABqtQfJYKPu09/wzZ5H8WleLrzxNkbNuojc3Tt4/a5b2fPdt2ia1t3VFX6BRMByClQWtr5Cc2VRgei/IgjCz8KGlRncE/4G+aE+LBNvJ7aiCjSNPtPOZNr8m7jhqX/Re9hIvnzxaT589A9UFhZ0d5WFXxgRsJwCRQcyWqzQrGkaVUUFhMbGdWPNBEEQOueNsQ+wJ2ojv+nzF7LjQ7CGDQRJonjLLgoy9rF79TeBBRaB3N07ef3O3+Bzi4nmhJNHdLo9BYoPtFyh2VVXi6e+nrC4hG6smSAIQsdya3NRUbkw+SI2ZqbzUK/nedr7W2w5n7L4mT8BYDCZiUhKZsiZZ+OIiCQ8oRd6k1huRDh5RMByChRlZbZsDirMBxAZFkEQery39r5FmDmMP056kJIBFVz9+TU8kPIqjyk34ApqYOC8aUT17oOsEzPfCj8d0ST0E/N53JTlHiK2b8sOt0gSwTGx3VQzQRCEzsmsymRC3ATMejNJ0fH8e8a/qZMbeDjlPSLPnUFMaj8RrAg/ORGw/MRKDx1EU1VifpRhqSoqJCgiSqzQLAhCj+f0ObEb7I3P03r356nx/6TMXMbvc24nrzavG2snnC5EwPITKzqQgd5oIvKYFZoBqgrzRXOQIAg/C8X1xcTYYpptmzxkDB/P+QAvHp7d9mw31Uw4nYiA5Se2Y91TRAzLIyP9HTyu2sbtVUWFYkizIAg9Xk5tDrXeWlKCW655lhSUxA1pN/BtzreUNpR2Q+2E04kIWH5icePKiB5eQWHdI6xZM5LlH03m+2W3U9+QSWicyLAIgtCzrchZgUVvYULchFb3z+4zG4POwLPpz4oJ44SflAhYfkIebzlGh4+0tGfo1+tlrMxCksGtX8aAyw9Ra36EPXvuoqjoI9ye4u6uriAIQgvf5nzL5PjJWPSWVvc7jA4eGv8QS7KX8Ny250TQIvxkTihgWbRoEZIksXDhQgB8Ph/33nsvQ4YMwWazERcXx/z58yksLOz0ORcvXowkSVx88cUnUrUeobZmOwDBwSNJTJ3BpHOe5dxL1nLGGdvoHfc48QlzqW84wN599/LDD5NYv+Ecioo+6d5KC4IgHJFeks7uit0kByW3W+7CPhdyz+h7eGXXK/xz2z9F0CL8JI57HpbNmzfz8ssvM3To0MZtDQ0NpKen89BDDzFs2DCqqqpYuHAhs2fPZsuWLR2eMycnh3vuuYcpU6Ycb7V6lNraHRgM4ZjNzZt+TGYHKQMua3zucuWxc9etNDQcpLRsObGxl5zqqgqCILQQYg4BoNJd2WHZ6wZfB8CTW54kwhLB1QOv/imrJpyGjivD4nQ6ufrqq3nllVcIDQ1t3B4cHMyKFSuYO3cu/fv3Z/z48Tz33HNs3bqV3Nzcds+pKApXX301jzzyCCkpLTt3/RzV1u4gOHh4q6ueHqumZhtO5z5U1dMiuBEEQeguKcEp3JB2Ax9nfcxnBz7rsPx1g6/jukHX8fjmx9lTsecU1FA4nRxXwLJgwQJmzZrFzJkzOyxbU1ODJEmEhIS0W+7Pf/4zkZGR/PrXv+5UHTweD7W1tc1uPYmmqdTW7SQoaFiHZRtcORgMYYwbu5w+KXefgtoJgiB0zsKRC7ms32U89MNDfHHwi47Lj1pISnAKz297/hTUTjiddLlJaPHixaSnp7N58+YOy7rdbu677z7mzZtHUFBQm+V++OEHXn31VbZv397peixatIhHHnmk0+VPtYaGQ/j9de0GLJqmUVGxiqqqdVitydjt/dosKwiC0B1kSeah8Q/hV/08sPYB9LKec5LPabN8va+eQeGDWHZoGQ2+BqwG6ymsrfBL1qWAJS8vjzvuuIOvv/4as9ncblmfz8eVV16Jqqq8+OKLbZarq6vjmmuu4ZVXXiEiIqLTdbn//vu56667Gp/X1taSmJjY6eN/arW12wEIcgxts4zHW8KOnTcB0KvXTaeiWoIgCF0mSzIPT3gYn+rj4a/vp3ZgAReMvQKLKbCga0l9CSvzVrIydyVbirfg1/yMjh7dYXO4IHSFpHWhO/enn37KJZdcgu6YNSMURUGSJGRZxuPxoNPp8Pl8zJ07l4MHD7Jy5UrCw8PbPOf27dsZMWJEs3OqqgqALMtkZGTQp0+fDutWW1tLcHAwNTU17WZzTpX9GX+iqmo9E8Z/3WYZv7+e79YMZdDAx4mNvazNcoIgCN2lMHM/ubt3UJydRXF2JvVVgQ642Qn1lE+LRCfp2F2xG72kZ0zMGGb0msH0XtOJskZ1c82Fn4vOXr+7lGGZMWMGu3btarbthhtuYMCAAdx7773NgpWsrCxWrVrVbrACMGDAgBbnfPDBB6mrq+PZZ5/tUVmTrqit3U5QUNvZFQBn/X4kyYDX13EPfEEQhJ9CzVeHqVsVWAvIcWYiQdN7IRkC3RsPbtvMJ397BKPFSkyfvgyYeAZbv/gUgLEzZ3MwqAqX38W8gfM4I+EMgk3B3fU2hNNAlwIWh8NBWlpas202m43w8HDS0tLw+/3MmTOH9PR0li5diqIoFBcHJkQLCwvDaDQCMH/+fOLj41m0aBFms7nFOY920P3x9p8LRXHjdO4nLvbydstt334DmuZDp7OdopoJgiA0ca4rbAxWAOpW5mGIsWEdGsnGT95n7eI36ZU2lDl/eBRVVfn86b+hMxi4+HcPkTxsJACKplHs8VGrQY3Lw9GUvVUnE2k0HFe9irJrKMquxu9V8XsV/F4Vn1fBXedFb9Ix9ar+mG3Hd27h5+u452FpTX5+PkuWLAFg+PDhzfatWrWKadOmAZCbm4ss/3In2XU696Jp/nY73Pr9dShKPSkpd5EQP+8U1k4QBCHAnRHI7sb9eSIlT2/FlBKCZUgEeXt2snbxmwCcu+AuVFXli38+zuHtW5h9zx8agxWAPx0o4D/55S3OLQEbxw+kl6VrK9LXVbr57OltyDoJo1mH3qhDb9JhMMoUHwyMBp0yVwxQOB2dcMCyevXqxsfJycmdmuHw2GNa89///vfEKtXNamp3IMtG7Pb+bZYpKg7MaGu1/jLmnBEE4eeldlUe7owqJLMOza2gVHnQ3H6cawvY+PrbAEy+6jpswaEse+5JsrdsZPbdD5AyYkyz8+xzukkwG3iyfyISEhJw0OXhvsx8av1Kl+u1ZflhDGYd1z46AaO56RLlcnp568H1DJochzXIeELvXfh5OqkZFiGgtnYHDvtgZLntPyq9PtCxKCJ82imqlSAIQoBzQyG1Xx0GIOau0SCBPsqK53At7qwqxkddSHVBOWNmX8qXLz5D5sYfuHDhffQZNa7FuQo8Xi6MDGFaWFNnyaDawCCKrk7QX1vuYv8PRYy/pE+zYAVg6/IcAEadm9TFswq/FCJg+QmUlHxOcPDIdsuYjJEAeDylWK3iD1AQhFOn+tNsACJvGYps0SMZZGLuGgWAv9pD8d82MTBhAi9cdxVebwMXLLyXvuMmtjiPqmkUun0kmJt/OTve0cyblx3GZDeQNjW+2fa6Sje7vstn9HnJWOwiu3K6EgHLSeauy6bfASd6/3dU7xwFjU1k2pHHGmjgtpsgCrzeMhGwCIJwSjmmJ1K3Ko+yf+8EIPquURiiAhO86YKMOMPq6VXRH2u4Hd90A/0ntL6+W5nXj1fTSPxxwHLkvisZluqSBjI2FDPpslQMRl2zfZuWHsJk0TNsxs9z1Khwcvxye752E0PBHhIL3Th8ZnR1FejqqwK3hhp0rjp0LieO4nzC8guIjDwHizW5u6ssCMJpJvicZOL+PBH71AQAyl/fjVLnBUCSJQb8/lwMA4OI7JXM2IvmtHmefHfgmPiTELBsWXYYq8PA4DOar6dWWVhPxvoiRp/fu0UzkXB6Eb/9k0xXtBus4dhvz249L+qqgn8MQDfuboYOuavlfkEQhFNANuoIPjeZTWompVv2oT6Tjt+g4fF78Wp+NL/K2LghJLRzjnxPIGBJMDUfYtwYsHQyYqkqridzUzFTruiH3tA8u7Lhs2zsYWYGTxELw57uRIblZCvYCvGj2m7E3fk+qH4Ycc2prZcgCMKPbNu2jXWb15NNET6jhl6nJ9weSlJoHHWyG2dU+xFHvtuHXScTpG8eZBydkr+zGZbNSw9hCzExaFLzoKT4YA2HdpQzbnYKOr24XJ3uRIblZNK0QMAy7ta2y+RvgfjRYBfTVguC0H28Ln/jvFm33vpboqIiG/f5/X62P/oo4b3a/z9V4PaSYDa2WDOoqUmo45ClosBJ1tZSps3rj87QFJRomsb6T7IJj7fRd0x0J9+V8EsmApaTqfJgoMknflTbZdzVYG1/uQJBEISf2it3rsEQGoLPVM3rr/2X/gP6AoFAoaSkBIDQ0NB2z5Hv9hJvajlqpzF86USKZfMXh3CEmRkwIbbZ9ty9lRRmVTNrwVBkWSyiKIiA5eQq2Bq4j29nSLOrGiL6npLqCIIg/JiieDic8yKJ01bQS/ZRXReKy3cWFRUVjWUcDgehoaHExbXfbyTf7WVMcMulRY4mXDqKV8ry6shOL+PM+QOaNfloqsaGT7OJTQ0mKU18wRMCRMByMhVshbAUsIa13FdfDuueg/IMSBjTcr8gCMIpsPq7QQDYoxxo1GELt3PmjPeP61wFHh+XmFvLsAQiFrWD4zcvPURQpIX+42KabT+wtZTyPCeX3DOyRXOTcPoSvZhOpqP9U1qTsRx+eAYSx0HaZae0WoIgCEdFRc0CID7hUgA0qaFxn6ZpFBcXU1hY2O45Murd3LznMDV+hRRry7WCmkYJtZ1jKc2p5dCOcsbOSkbWNV2KFL/KhiUHSR4STlxqSCfflXA6EBmWk8XvheKdMHRu6/tVH0gyXP3Bqa2XIAjCMRIT5lNXu4v8/DeObAnkQVRVZfHixWRmZgJw4403kpDQNKi5XlG4bW8ublVlXbWTWJOBx/rGc15EcIvXONrlpL0My6bPDxESbaXv2ObZlX0/FFJb7uL8W4Yc93sUfplEhuVkKdkFirftDreqArKIDwVB6F4hIaNJif5343OP08ATf36YRx99lMzMTC644AKio6P5/PPPm2VIHjlQyOrKWnyqxq/jI1k1ZgC/TohEbqXJ5ugWtY0ES/HBGnJ2VzD2gt7NOtT6PAqbvzhM/7ExhMfbT8bbFX5BxBX0ZFn5WOA+po1vBapfBCyCIPQInnoXmZ8kkzAigrzMEOrNoNdJXDpnDmlpaQQHB/POO++wZelasnpL7Dcl8mZhFX/rl8D18REdnl+i/X4nm5YeIizORp9RzYdN71iZh7vex9gLe5/Q+xN+mcQV9GTJ/jZwr/hA37JNVwQsgiD0FFEpfWgotdA75SYwF1Gamc3/3baA4NAwirPyWfPhCgDqNhRyl70/UMXNCZFcF9f2iB1VVVld5eSLsmq+q6wDoNLna1Gu8EA1eXsrOeemtGbZFbfTx7avchh8RjxBEZaT+4aFXwRxBT1ZLKGBOVj+lghT74Vp9zXfryog61o/VhAE4RSy2B3E9u1P9paNFCgykqIgKzKf//t9thXtw6aZOMs/lGCdGYBJplz+3Hc4EOhIW+b1E27Uo5MkfIrKw9mFvF1YgedHnWyz9/yG1fv2I0kyoEOSZHy+SuKG3U6fEdOblf34neVUWYoZfd6EU/EjEH6GRMByMriqArfRv4at/4VdH7YSsIgMiyAIPUevtOFs/XwJVX0Hg07H8889j4LGcH8yQ5UkDgwqI+zsFNjnJk9pyqzcm5nPm4UVWGSJi6JC+aS0Co+qEabXsSAhgvNCdfx2x2aqVTPnplyARToXDQVNUynMW4GPSvqOSkQ6JrtSUVJFZlE6mAC9H2g5VFoQxBX0ZChID9yP/y3sWAxaK33jRadbQRB6EF9lA6Y+k4BqAHor0Yz09yYvthz1kjj2V+zmd988BfH/Itdv4+EDBZwfEcybhRWMD7axoaaexcWVOHQyj/SL57q4cKo9dcze+D2FajBvDQiif+yMxtdTVYWMXf9DU/ozaNrFzeqy9KNvGx/n5uYyYMCAU/ATEH5uxCihk6EgHczBEN4n0OxTWwBVh5uXERkWQRB6kH5J4zjfO4L+/jiudE+iKDiXostUlIvCuGXTbby2+zVuGHQV2ZP68ufUOP6TX8Zl27MZbDfzyuBkACaH2MmcMoR4inlqzzLmbF5LlhrPA3FuJsYObfZ6e7YsRm/NISXpd8hy06WnqryWwyX7SI4cRFBQEDk5OafyxyD8jIgr6MlQsKVphebpf4Av74PnRsHVH0KfI+20ql/0YREEoceInzWcWksuk0NjmLXjcjRJ47W9nwIwLWEar579KolBiQDcnGhjmMPKn7ML+XNqPBFGPWl2C6Wucs5du48d/kR0WhSKZCBWKmdW4vBmr6X4PRSVP4/qHk3f6dOa7fvio2/Q0Jh12Vms+WGlCFiENokMy4k6ukLz0Rlux98C130eCFBW/bWpnOoHSQQsgiD0HEFn9mK57js0qamz7DPTnuG5Gc81BitHjQux88WofowKtiFJEr+PKiHLbaTCp3K3tojPQ1/gHa7kvZjVxFibD33esek/yMYy+g/4fbOp9msq68gu3EuviH5ExoSSlJREUVERHo/np33jws+SyLCcqOpcqC9rPmFc7ymBe8sxK52KPiyCIPRANw25CaNsJKs6i9tH3E60LbpTx52ddAb/yr+UUK2MtIF/JTx8Crm5r5F14DF69boJqzWJnKxNZOx5Fkw70Rqm03tm84k1v/joWzQUZl16FgBJSUlomkZeXh6pqakn/b0KP2/iCnqiGldoPuYPsbYocO+th/S3Ao9LdouARRCEHkeSJK5Pu/64jr1o4keN5wCw2foAUFj0PjVVmVRVrQGTDck/mGFj7292bF11PVn5u0kI60t0fGAUUkREBFarldzcXBGwCC2IK+iJKtgKIb3AHtm0zVcfuM9ZG7gdNfDCU1s3QRCEn9CPV1IODh5FcPAo8vL+i6rocBaNYOKMvxIVn9Li2G0b9qJJfmaeP7XZ+ZKSkkQ/FqFVImA5Ucf2XzkqJBmQYNY/YNQNTdvFMumCIPyC6fV2Ro96H4DXfr+W3kPCWw1WALxeLwBhkc0XT0xKSmLFihX4fD4MBsNPW2HhZ0V0uj0Rig8Kt7dc8FCnB0cMOEtAlptuImARBOE04PcquGq97P2hiOxtpVQW1VNVXM/B7WUUH6wBIGN/JpKmx2xtvpRJ7969URSFQ4cOdUfVhR5MZFhOROk+8LsgYXTLfY5YqC089XUSBEHoZrJOIjY1mKIDNXz50u5m+3QGmbNv6UdZQw6R9l4Yjc2zKFFRUURGRrJ792769et3Kqst9HAiYDkRBVsCQ5VjhrbcFxQnAhZBEE5Lsk7m0ntGoakaLqePqqJ6kMBg0rH0+R2s/GAbAFXOshbHSpJEWloaP/zwg2gWEpoRTUInomArRA8Go7XlvqA4qCs69XUSBEHoISRZwhpkJL5/KPH9QolKCmLomYk0HIlTHObQVo8bPHgwXq+XjF37TmFthZ5OBCwnIn9ry/4rig8qspvuBUEQhEaDJ8eh6NwAxMUmtNjvqq1n9XtfAlC87uAprZvQs4kmoePlqYOy/TDxtubbV/wRNrzY9FxVxJT8giAIR1gcRrymKgB8fm/j9i2fr8Xn9fHDrk24NS9THENILQjB62zAaG8liy2cdkSG5Tj517xCZaYF58at+PMyAlP0A3hqIaIfXPsp3LJWBCuCIAg/kjQ8MJQ5bUwKmqZRVVDB0q3f8NWu77DpLVw2YgbJpXYavLVkb9nUzbUVeooTClgWLVqEJEksXLgQAJ/Px7333suQIUOw2WzExcUxf/58Cgvb73z6yiuvMGXKFEJDQwkNDWXmzJls2tSzP6TVX66lJD2EvGe+JOusi8kaPZC8C0ZT9uZnuMqlwKKHMUO6u5qCIAg9ztyrLiM1NZWPP/mY1157jUM7MgGI0BxMdw7EsUHBrXOzXb+G/ZvWdHNthZ7iuAOWzZs38/LLLzN0aNMImYaGBtLT03nooYdIT0/n448/JjMzk9mzZ7d7rtWrV3PVVVexatUq1q9fT69evTj77LMpKCg43ur95FyeOKxjxpCy+GXi776a4MlD0NBRmWWneG3HxwuCIJyuZFnmqquuYs6cOeTn57Nk01eESXbGVkVgx0yVoYx+D55D/2lncHh7Om6ns7urLPQAx9WHxel0cvXVV/PKK6/w6KOPNm4PDg5mxYoVzco+99xzjB07ltzcXHr16tXq+d55551mz1955RU+/PBDvv32W+bPn388VfzJuXfuIviiizANn4Jp+BSCjmwv+fvjOFev7s6qCYIg9Hg6nY60tDT2btvN3uz9jI0ZSuhwM6wH+9g49FYTRmsUquJny7KVTJ7b/hdf4ZfvuDIsCxYsYNasWcycObPDsjU1NUiSREhISKfP39DQgM/nIywsrM0yHo+H2traZrdTxVdSgr+0FPPQlk0+ssWM6nafsroIgiD8nF02dw4XeEYRd9hMuDERgIMrNvH0vKv56sVHQbKTv1/r5loKPUGXMyyLFy8mPT2dzZs3d1jW7XZz3333MW/ePIKCgjosf9R9991HfHx8uwHRokWLeOSRRzp9zpPJtWMHAJahw1rsk8wWtIaGU10lQRCEnyWdSc/Ay8fiXFeIc30RMqB5vWhqLWMv/g0m+0DSv8pHVVRknRgncjrr0m8/Ly+PO+64g7fffhuz2dxuWZ/Px5VXXomqqrz44ovtlj3W448/znvvvcfHH3/c7mvcf//91NTUNN7y8vI6/Ronyr1rF/roaAzRUS32aW5XYN0gQRAEoVNsI6OJvm0E2tz+fFL0DTuqViPJFibMOY+4vmEofpWaMld3V1PoZl3KsGzdupXS0lJGjWqaLE1RFNasWcPzzz+Px+NBp9Ph8/mYO3cuhw4dYuXKlZ3Orjz55JP89a9/5ZtvvmnWmbc1JpMJk8nUbpmfimvHTiytNAcBKHVOlKoqNJ8PqYdMKa1qGoddXnY5G9hd52JXnYsQg45/D07u7qoJgiA00htlJPMZzL7tBhIGhKM36AmLswNQWVhPaIytm2sodKcuBSwzZsxg165dzbbdcMMNDBgwgHvvvbdZsJKVlcWqVasIDw/v1LmfeOIJHn30Ub766itGj25lMcEeQlMU3Lt3E37rLa3u10dEAOArLMSYlHQqqxZ4XVUjs8HNrroGdtW52O10scfpwqmoAMQYDRR7fQA8P1BDL4sVpAVB6H6KovLlS7vR6WRShvdCZwhkqq1BRiwOAxUFTvqMbJnVFk4fXQpYHA4HaWlpzbbZbDbCw8NJS0vD7/czZ84c0tPTWbp0KYqiUFxcDEBYWBhGoxGA+fPnEx8fz6JFi4BAM9BDDz3Eu+++S3JycuMxdrsdu91+wm/yZPJkZ6M2NGAZ0noGyD71DMqefhqluhq6IWCZnZ7FtrpAH5oUi4k0h4WZ4UGk2S2kOSxEGg18XV7D/F2HqPT5iTL1jCyQIAinN7fTh7veR0SivTFYOSo0xkZViegbeLo7qVPz5+fns2TJEgCGDx/ebN+qVauYNm0aALm5ucjH9PN48cUX8Xq9zJkzp9kxf/rTn3j44YdPZhVPmHvXLpAkzD8K3I7SHRkNpVRXH9f5N27ciN1uZ9CgQUhS17If9YrC9roGft87hpsTIrHrW59lN8IY+LWXiYBFEIQewhZswhJkxGhueVkKibZSmnPqRoIKPdMJByyrj5lzJDk5GU3rePjZ6h/NU3L48OETrcYp49q5C1NqH3T21ttSTzRgWb58OQBRUVGMGTOG6upqCgsLCQ0N5cILL2w3iMms96ABZ4YFtRmsAEQYjgQsXh9gOa56CoIgnGyTLu3DN//dR/HBGqzBRhyhZiRZIizORsaGYvxeBb1RLHdyuhLDWbrItXMn5nY6BMtmM5LZjL+qCk3TUFWtU0HcUUlJSZjNZmpra/niiy/YuXMnkiSRnp7eov/Qj+2vdyEB/Wztj+CKMAayKmVef6frJQiC8FNLHRONNdjIR49v5a0/rGfpCzvxexUSBoSi+FUKs6q7u4pCNxKrNXeBv6oKz759OGbOaLecPiKC0r/9nSVvLOXPY69HkyT0soReJ2GQZXQ6Cb0sN23TyehkCXe9k0FKHVP6xHPllVfi8Xga+/B8+OGHLF++nD59+mCzNWV3NE0j3+Oj2ONje20DBknC2sFcBVadjE0nUy4CFkEQehCdTmb4jF6s+/gAw85MZMfKPLatyGX0+cnYQkzk7quk1+DODeQQfnlEwNIFNZ99i2XK7/HmlrdbLv7ZZ3Dty+CRLYFh13+5cCDodPgVFb+i4Ve1wGNVw68euVc03l1XQ65kZ+zYsRgMBgzHDIs+99xzeeGFF/jyyy+57LLLAHC5XGTl5HB2UdPMun2tnRvqHWnUiwyLIAg9zvCZiSQPDSc0xkZ9jYeszSWMOjeJxEFh5O2t7O7qCd1IBCxdoE8YgT68EI1UKt79jvB5U1stt6vMxQNr68BsYpq/hGsnzerU+VelZ2AyhdO/f/8W++x2O+eccw6ffvopAKWlpZSUlAR2Tr2YMfh4bdLwxv4pHYk0GKh3lsOudRCdBhH9xIR3giB0O0mWGudb6T8+hi9e2MnLC9cw47qB7F9XRG2Fi6Bw0ffudCQCli7wl3ox9nLgydpBQ3ocjqkVGONbpidfW76TStnGW9PDmHJO54IVgGCzjkqX0ub+YcOGkZGRQX5+PklJSYwfP57k5GTe37iPcJ1GpLHzI34ijHr6HVgGOwJDyzEHQ8IYSBgLiWMhfhSYO7+cgtA1iqKS/mUOuXsqiE4JJjktHE0FZ7WHARNiujxCTBB+iZKHRNBvbDSZm0qITHSgN+nI3FjM6PN7d3fVhG4gApYu8OY7sQwKJ/j8mZQ8u53S51YRv+iyFheXEJOM3eNlyjkTunT+UKuenNq2AxZJkrjiiitabLdrKtVK1xYHizTqOVhfD4B29UdIhemQtwk2vAir/wpIEDUoELwkjkVLGIsamoxOJz4yJ4Pfo7Dp80MAFB+sZcc3TUtLxKQEiRk9BeGIKVf0I29fJZuXHSJ1RCT71hcz6rxkEdSfhkQbQCcp9T6USjfGBDum5HgsAxUgmqoP1rYoG24zUq1rf6ROayJsRuqVrv9K7GjUql0LWIYHWTlsjgcg2xgJU38P13wIvz8ECzbB7OcgYRTkrodPb0V6fhTlf43iVx/NZvH+xRyqOdSl0U9CcyargUvvGcmA8THojTLn3TKEkecEJhr0edoOWgXhdGO2GRh9fjJZm0tJGBhGbZmLwszq7q6W0A3E1+VO8ubXAWBMdAAQcdOF5N/5DvUbwnH1y+T8N7fj0hnRawqKZMGn16P4FXTtzIfyY5EOMx7Ng9vrw9yF5p1gWaKgi9e4ebHhDB2QCrtBOjbwkGWI7B+4jbw2sM1VTdbmF+m78u9oZfv4+6a/49f8RFmjGB87nnGx4xgXM45oW3TXKnGai00NQZIl9m8oxhFmJizWRvpXOac0YFGc9SjVVRgTEk7ZawpCV/UbG8PmLw5zeFc5wTFWvvs8m3n9e+4SLsJPQwQsneTLq0O26tGFBTInkiQR+dsplL2Ugfu9Lbh1FoYqVUyOs+BTVGLCbF0KVgCiQqxADfll1aTGR3b6uBCdTMZxJDui4gYBUJa/kz7Jw9suaAkhccytsPLv/F/KJQyY/jBbS7aysWgjG4s3siQ7MLtxclAy42LHMT52PGNixhBsCu56pU4jPq9CZWGgWa6yqJ74fqEA+L3qKXn9+g0byb3+egAG7NsrUuxCj2W2GRg3O4Xv3s0AoF7SSM+tYmSv0G6umXAqiYClk7z5TgwJjmb/1M39kjD13o6Un8iohmycFhsL75l73K8RH+YAiiioqO1SwBJq0NGgdv1iExUWxyF7CpZd78Hk+e2WNVtCqdLp8VcfxmqwMiVhClMSpgBQ5a5iU/EmNhZtZF3hOv6X8T8kJEZEjeDi1Is5O/lsbAbRJ+PHlr24k/z9VQBUFDhJHhLowH0qMiyK00nBPfc0Pa+uRh8q/vkLPdeA8TF88MF+6k0SWdF6Nn6xjw9vmSAC7dOI6MPSCZqm4c2rw5jQciHGiFsvQHMXMMWRyG69jbLiuuN+nR2HSwGwmY1dOi7cZMCjN+Dzd31elcKh8xlWso7C4qwOy1aZrMi1hS22h5pDOSf5HP444Y8su3QZX132FQ9PfBiz3syf1v2J6e9P54HvH2Bj0UZU7dRkD3o6TdUagxUAvVHXOOW43/fTBSyaqlK3ciW519+AWl9P/D+fDbxmUdFP9pqCcDLojTr2pVnZGiNzz8WD2JpTxbJdxd1dLeEUEgFLJyhVHtR6X2P/lWPJOh2Rt5/B1DgPKrDi+9zjeg2ny8N/t5SRFuJndL/ELh0bbjKiSRKl9V1bzfTQruUM2/g4edYEwsPiO66jJQRzffuT5gHE2eO4tO+lvHTWS3w952tuHnozu8p3cePXN3LuR+fy2u7XTusOu6rqpzzfCcBFd45gwb/PZOwFvdHpZWRZwv8TZlgKFt5J/m8XIJlMJL70b6wjRwLgK2wZiApCTxNk1pNb2cCUvpFM7x/Jw5/vxun2dXe1hFNEBCyd4M1r3uH2x8wpcQy6/XwGGw18m1l6XK9x1wsf4VT1PHzpqC4fG2UJ9KsprHN2/iBNg1WPYtD8mG9ehclo7fAQryMKh6trK6bG2GK4cciNLLl4CW+d9xbDo4bz9NanOVRzqEvn+TkrLPqQtT9MZvfuO9i58xa+WzOErANPIMsQ07v5XDd6o4zvJ+rDotTVUbdiBVG/u4fkd97GNnYsuvBwJKMRX6HIsAg9X43Lx4SUcL7dV0KNy0dZnZcHP93d3dUSThERsHSCN68OXagJnb39pprpiWGsq2vA7+vaBUfTNHZUSMTLNYzu1/XRGtHWwKyPJQ2uzh/krad35W5MqpfIkKjOHRMUT4THhaZ2/YIqSRLDo4bz+zG/x2sewlcluSi/sCyLpmkUVLv4ak8xT63I5OU12QDk5LyEqrppcOXg99cRFTULF+9gjdneYuVZvUmH3/vTZFjce/aApmGf2jRDs3P1ajSvF1/JkdS64ge/9yd5/dZomka500Ot24dfEc2FQvtigi1kltTx6ze2kJ5bjUkv8+n2QtZmdZz5FX7+RKfbTvDm17WZXTmWJoEeia72AZMkiVC7GaWLc6kcFWu3AWWUNrg7LNvIFOiPk957NiM7eYg+tDc2TaWmLp/g4F5dridAuDmcmqjf80ghPFe2mzPDgpgZHsTUMAehnVxWoCd65PM9fLKtgOqG5unpK4Z7aWg4SNrgZ4mOvqBxe/b2DCKHfoTX81uMpqY5e/TGny5gce3YiWyzYUxJadxWvfh/yFYrwRdeGNjw5b2w5TWYcjckjoO+Z530emSXOXl17SESQi0s3VHE3qJA1i7CbmLtvdMxG7o2uk44PWiaRnWDl9I6T+O2vY+cw5yX1vPst5lM7hvRjbUTToWf7xXiFNEUDV+BE8ugpA7L7iyuZbDFhE7f9cRVsFlPfu3xLUYY6wiMwCl3ezoo2dye8FH4u/Bt2hYRWOOosmTXcQcskiQRpeZSIcVybVw031TU8GFJFTIwJtjGzPBAADPAZv7Z9P5v8Pp5a30O5w+J5aLhcQyOC2Z3QQ03vrmZnbsWYDH3IiLizGbHjBz/CBmHL2f/tk8ZOv7Kxu0uSw2bG7Lw5hWgl/UYZAMGnQG9pMegM2CQDehlPUY/RPgtyFYrssmEZOh43p7677/HOno00jFrRrkzMgi9eh7mAQNAVWHHYtBUWPNEoMCfqulyBN6G3QU1zH5+LZIkNQbnY3uH8dxVIyipdfPoF/vYX1zH8MSQk/J6wi9HjcvHHz/bzfdZ5Tx2SRp/+GQ3ellCp5O5cXIKC95N50Cpk9SolgMjhF8OEbB0wFfagOZTMSa0n2FRVZXd9W7m9Dq+pc9DLHr2Vx7fN2uLwYBB8VPRxW/mdWF9ic//Hk3TOhUchEalAeAs2wf9Or9G0o/1MWtUu+q4PyWW+1NiKXR7+baylm8qannqcAmPHSwiDpmF5RKXJoZj7heGPqzrMwf/VBRV41C5kz2FtewprCU9pwq/qrFgeir9YwKfk7K6ei5M+RKvJ4cRw99Cp2veRyghZSR7M4Ko9uxvtv3b8PfZ698MKzuux21LFM7YcyQrp9Mhm81IZjOyyUTo/GsJPzLHCoB7714atmwh7oknmt5HdTX+4mJM/QcENmQsA68TblgOJXtg2T3QUAm24/tM/9g7G3NRNZg3NpHLRyXg8auM6x2GJEl4/Ap//3I/u/KrGwOWklo3+4pqmdov8mcTvHaW4qxHZxdD/Tvr4hd+4FB5PU/MGcrloxN5akUmFU4vWw5XMnNQFCa9zOqMUhGw/MKJgKUDVR9lAmCIb/8PoSi/jipNY2jvsON6nVCrEZdy/L3drX4flVLXmpSs/WaSmLWY7IL99EkY2GH5sIiB+ABPZfZx1jIgyKDH7zY1Po8zG7k2LoJr4yJwKyrrquu4Of0gW+p9nLkkG9Rs9FEWzP3CMPcPxdQ7GOk4sljtUTQNVQOD3MqFUVWhdC/Jz+QAYDHocB0ZehwfYmFwXBAPzhpIv+imz4hd3s/FqcvRJCuhoa2vKaX5YvCS3/ylZD/Jan9ev/Lf+FU/PsWHTw3c/Kq/8fGvvvoVdUOSiL/xdlS3B83tCtx73FR//AmurVvhmICl/KWXMSQmEnTeuY3bar74AgDzgCOrg+94DyIHQq8JYDxyMf1sAcxbfEylNSjYCjk/wPjfgq7zMzLfc3Y/3tuUy/iUcEb8aMIvk15H/xgH32WW0eBV+HJPMdtyqwF4au4wLh35y5mJtyF9Gznz52MZPoyI3/wG2+TJv7iA7GQ6UFrHofJ64oLNPPNNJvd9vAvLkWbDtzbk8OTlwxieGMLmw5XcOCWlg7P1fJqmkZ5bzZe7A82lNS4fEhKyLJEcbmV4YgjjeoczKO70W5xWBCwdsAwIw5fvRHP7wdR223rG/kCnr4EDj68dNdxuwou7y9PyH2XTVKr9Xeu02G/wWfiW6cjbvbxTAYusM1BmMKHWHN/Q7aNciopE63U162RG+HTU62DM0BjirozDnVWNO6OShp1lONcWgF5GF2TElByEbDeiDzVhGxNzQkHMiHV7KPX6eWpAIhdGhuA4OkuxpgUyDVte5bAZPlEmUX7W8wyOC2JQXBAh1tY7YidGj+PgfqjV39TmxUhTNeQf/ao1WcOq2oiwtP85CqkHkhIIOu+8FvsatmwFqelnoTY04Fy5ksi770LSB/7kvbm5lPzlUQCMycngbYD9S2HYVYEmoKMdooNiA/f15YHmom1vQ9m+I29yPPQa1249jxVuN9ErzMqOvGpmD4trsX9YQgjvbMxl7YFyzugbyT8uH8an2wv4z/eHfjEBi+rxUPTgg5j69EHz+ci76Wai7rmb8Btv7O6q9Vi9I+wMiHGw/5g5rpyeQPP5Z9sL+Wx7IbdM7cNn2wu6q4onza78Gh78bDc78qqJCTIzPDGEXmGBLw9+RSWr1MnyXcV4FZUzB0Qxb2wvpvaPxKA7PcbPiIClA9YRUdR+k4uvuB5dsKnNcgcKa9EDSQnHNx19RJAFqKG4so7kmK5naeyoXV4A0WwNYX/4MCyHVwF3deqYOr0Bk6cLw6dbkeXSCKOqzf3b86rQJImRSWHIZj3WIRFYh0SgaRq+onoq3tiDUunG7zDi21uJ5vajj7RiTg057jolmU2Uev3ctT+Pfxwq5qtBCiV5r9Nv3QYMRXtB0oGmcInuBzij429xep2eGk8ILrXtYe6SFoaiVf5oqwa0/21b0zTqzBqhxjY+a6rarN9JzedL0fx+HDNmNG5z7w0EHb+/QUfyd3fy3IRHAjucR+p7NHNSug/+dy1kLA+cc8AsmLAAltwGeRsgehCYOu6QftSwxBB25FW3uu+OmX2ZMTCK8SnhWI2Bf002k45b3k5nbVZ5pztVaqoGEt2atVi0bB8hViMXDI0lPsSCfCRz596zB+/BgyT860VskyaRMXIUktnSbfX8OdDJEsvvmMKyXUUseHcbAONTwthwsOlv56PNO6lwWfD53BgMPaf5uCt2F9Qw96X19Imy8dr1o5nWL6rxc3Msj19h+a5iXlpzkBvf3EKvMCvXT0zmspEJBFu7/mX35+T0CMtOgC7UjGTU4Suub7fcwYp6EvT64450o4IDfRyKKo9vptwgCWo7uNC1xtV7OoPLNlPl6jgI0TSNEK8LLajjSebaUuhyUyT1oq+l7boeDQxL85vP+SJJEsY4O+gkJJOOqFuHEb1wBAAej4fc3Fz8R2b7LSwsRFE636fnougQTLLEc6kWCjw+/pH+L5KXv4GhaC/qBU/BHysocAzt0nt1qWH4vWVt7pcJR5Oqm23TNJCl9j9DqseDKoHe0FYArcGRf3SaolDx2qs4zjoLY2JgQkJfXR3Z9wUCVI8BVuevZnnJRnwAFQcCp4geHLjPXQ8V2XD2X+DuDLj8v5B2aWDfij/CogQ4/EO79T3WsIRgdhfW4GtlCHOUw8yZA6IbgxWA6QOiSA638qv/bia/qv2JERVFZd3HB/jXglW8es/33TY5YXaZk5fWHOTvX+5nyuOrmPHUd9QemdzMMmQIurAwnCtXoVRVg9+PIeH4/55OF5IkMWtoHB/cMh6ADQcruW1qNGen7AEgyFTHjWlv0tCwv73T9Fhev8qt72ylX7SdD2+ZyJkDolsNViDQfHrxiHiW3zGFpf83mSHxwfx12T7G/vUbfvvOVoprujBa9GdGZFg6IMkShhgrvqL2A5ZDdW6SrW1nYDoSExro/1BSfXzZi2CdRI6/6wFLwsi52Df/g40b32HctN+0W7bKWUSEolASdvztxC/lHARgbFjbKztP6RtJ9MECvi2tYTotmw40j4JkkFnzzWpWrl0NZuCDbwGYM2cOJpOJd955B4PBQGqwldIt67n+Hy/iCG/7G3qM0YBH1RgWFAK4KCSBkrFnk7J6OfVBFhySRGGfK4jfvhNXvROLrePOfX7CQG17fgizJQa3tApF8aPTBf4UNanjZr3qkhw0WcJK681RmtrUibrum2/x5eQS/+STjft3vP8vbG6VHWenkJKWQFHROn6/5vc84LBz1Zhjmib0JghJglt/aD5SyGiDS16GT24OPK86DMmTOqw3wPDEENw+lYziOtLiO85GmvQ6/nrJEOb9ZyPlTi8Joa1PcHh4VznfvZtBQ01g1Jun3k9FQT0RCXZcikqxx4dNJ+PVNDZWO4kxGQg16PFpGn5VC9wfufkUlX5WM0m24/um/tHWfILMer65aypLdxbx56V72bf7IAnvv4p1zBg0rxddaCjS0S83v7D5iH5KY5LD+fc1o6iu2UWscjOD+0Zzy4ypDO89k9XfPUFt3U6Cg4d3dzW7bPnuIvIqXbx63ZguDetPiw/mhatHUlbn4eP0fF774RBnPfUdd5/dj/kTktsMen6uRIalEwwxNnzF7X+7y/H4SAk9/tRubHigA1VpTdem1z8qVK+jQe56/CnrAxc9l6/j4c0VxYF0rDVyQJdf56jbkgPf8svdNW2W0VQNrwSmNv7YNJ+GZNJRkttydtYPP/yQd955BwCfz0fm4Vy8rgYy1q1pt17RpkAqVdEFMUJ/CKexP71HPQ2Ap2QLALaYVABKczPaPddRki4So1TR5v74XlOQDS4O7F7V9N40kDvIlC1f/xaSqjFxQhsLbWoaSDKaplHxn/9gHTsWy5AhjbtrP1vCgT5Wrnh2KRZDUwDw14gwPHHHzMojyUfO1Up9hl0B8z4IPG5of9Iur9tF6eFAoJoWH4xOltiRX93uMccKtRnQO3ZR3UYW8L0/b+SLF3ai08vMuX80v/nnNAxmHYd2BLJbl2w7wISN+xi6bg+j1+9lwb5cLtuezZmbMzhnSyaz0rO4eNsB5mzP5sodB+GVPWS/tKPT9TuWomp8nF7A7OFxRAWZuXx0Aia9zKE//ZnaJZ9T/NAf0YWFEXrlFU0TMIqApUsmJVUS6bkFozGCmZMXM7LPOGTZRHDQcCorvu/u6h2XPYW1JIRa6Bfd+ebVY0U6TPxmah++vOMMZg+P4+HP93LlKxsorf1lZVtEwNIJhlgbvrIGtDZm4nQ1eCnWVFIij39IXVSwHQmN8rouzFZ7jDCjAZdej9rFWWiNJgfVegfxu97scAZbZ2kg/RoaPey46ggQaXYQpBTyXSvp/UXrsunzl6/5vxV7qZI1JseFtHoOTdWQdDJjRo4GwKDpOGfIVH79618zduxYzjnnHH57660AKPbAt/jyvJx2mwiUPdu46Kt3ycnPo1IxEGY0I9miUWUJrTbQmS8iMRCoVRV2vFAkgMEYhVXfdl+d5AGT8TfEkHv43ab3hkpHfVg2lm2hb7WF+MQ2OkofCTIaNm3GvWsX4Tf+unFXefY+YjMr4LxpSJLEoimLWHzuG03HxgxuemwOgVYWu2zU72yqgvVsrXiekuLP2yy26dMPeeve20lf9hnvP3gn1xe8R+aq5S3KfX1oFX9c/SLrc/c0+1395Yd/Ykl4h3cPPYGiBpr5FJ+Kz6NweFc5lYWB7OfwmYlEJjrQGWSS0sI5tCMQSM0ID1wEXk9LZvGwQHYw1mhg2ci+fD26H6vG9Of7sQPYMH4gX/ZLZmCtSr/S45vtd+2Bcopr3cwZFQjMHWYDTyc3MCw7nbLb7iN15bf0+WIphrg4PPsC/YhMqanH9Vqno23brmPT5sAkh1GR52AwNGXpIqPOoaJyLT5f239zPVVKhI3Cahdf7i7udFPmlsOVvLX+MA3epvm7Qm1GHrtkCO/eNI6cinouf2k9eZXH9yW4JxIBSycYom2gaPjLXGiqhupRUJxe/NUefGUNLP0qMMx3Q3U9h8rrKapxUePytdpO3xa9XodZUqh0dm3yt6PCTQZUWUeVu2sRtSMokkNj76RPbRaV7vaDJW/FAfxAcETHI4raE6bz0qC2/Oh9ursIpd7HF6tzsKwuZt+GNi6Wiopk1hE/MJlURyI+SSEzO4vExETOP/98JkyYQERkZGPx0Rdeyp7vvuWH/70NQE1NDRUVFZSXl1NXV8fWLz7jh2f/Rr9De9m/bT25ahxjgx0gy7iNEtLRgCUmEbdmwF3SuWHdVnMMdoMTl7f1n6ssy9iMZ4FlY+M2DQ2pg4ClUK4lRmlnDg9VBVmi4tX/YOrXD9uUKY27Nr30F9wGmDgv0IfFrDdT8NGvALjBmoLJcsxw475nga8eSlvvF+Cry2NfPwfVdpXde+/C56tupSoKxdmBqQFWvfEKzsoKDFYbUsbGFmXvXnM7n+T8i5tXXcmI189k1KsXkPb6GHbVv0+oIZFNJd/x8PqHAVj9XgYv3/EdX7ywk4QBodz64nTSpjaNJEoaHE5Zbh0VBU6SLIGm2jPDg5gWFoReghiTgZHBNoY6rAy0W+hrM5NsMVGzPdDpWIo6vmzpB1vy6BtlZ9iRzve1lTWE/Psf7I0fyNjfXI0hLq5xkj/VE/hb1wWdfsNTu8rrreTblX2prFrbuK207OtmZWKiZwMaRUWfnOLanbiLR8QzKTWCW97eymNf7OtU0PL8qgM89Nkepj6xmsWbcpsdM7FPBB/eMhEJuOxf68goPr6+kT2N6MPSCYaYQNq85Jn0VveXEPg2tvRAGUufXN1sn1EnYzXpsBn1WI06rCY9VoMOm0mH1ahvujfq0CSJgvIatm3bhtFoxGQyYTKZGh8fvdfpWrZxRpnNUOeiqLaecGvHCxkeS1dXRIE5mnhL+8dp1TmUGc3E6k7sY+NHQvejOWPcfoXiwzVYHEaePG8gH3yeweMZRax6+BuCZZmDHi9xZiMPnzcQswbGeDsGk4Erbp/Pk489TpgtpNn5lv0vkLUIM+iYes2vyN66idryUrxeL88++2yzTJR93xYcoWFU1tdzsKIKLUZmWkwfAHxWG1JdoGlB1uko0cVAVecWbgyyx6K6obiqgN7RrX+L9nnqUeXmF6yORrcMVmNY4TjQ5n4NDbXOSf2a74l7/O+N56sqzSXmy20UnjOUEceszl2XOBoqNzL/rGebn8h+pJ+R0jLboGkaazZPA4uOocmPsPPwnygv/5bKyh+QZRNK6QIKs4o5sOEZXHXVjL/0CioK8ph4+dUs+XodFV+/S1VNHaHBTSnwd879iKu/vAyrbzjRtmhy67KQ1VDGRs3g3xfcw+KM9/j75r8zJmYMvtJA36a4viGMPq9lW32fUVGs/SCL7G1lyKMDwcPRQXQSEiqtXxCUA9UAGI5jtEVNg4+v95Zw91n9kCQJv6Ly5W9+R193PWn/WYT5R0tPHB1irnWhc/jpyu+vgSNTIQxJe5H6+ixCQkY3K2M0hhMZeTYFhYtJTLzhZzW3jdmg481fjeW/6w7zyOd7GZoY0urQ/2PpZZn+0Q4Gxjq47+NdbM+r5rFLhqA78reQGGblg1smMv+1Tcx9aT1v/Grsz34WaRGwdIJsNRDxqzT81W4kWUIyyEj6IzeDzE16meleLx6jDq9fxeVTcHkVGrwKDV4/9Z6me5ev6XlZnYecCv+RcgqyLKM5K/jss/ZXH9XpdC0CmXyDGRIH81heGX3qPNh0Omw6GatOxqHTYdc33dt1OhxH7m06mRrJQILi7jDdZqorodoSROwJ/jwVTUL/owvGX9YeQFJh4uAoLhiZwFlpsbz47g7S86spUVVGRTlYXVzLHR/u4EWsWIcHMih7ftiOBx+jJjb/53X48GGMqsJt9/8RT0M9FSXF9J95PnV1daiqSrBepubIvDVRfQdw1vU38fKTi6itdRNKFalBgWYvzRFNeM4BfM4CDPZ4qk1xmJ15nXqfkUEJlJRDaXVemwELulpUb1NToobaboZFVVU2+DNJUdvpsKpqeA8fRhcZ0WyelvXP/IE4Dcbe+Viz4pOGXAPfbWRz5mecN/aOph2eI9/KzO13jg2LOQdDwT/Zu+/3jdv2v38WPtdaFHd14DWuuLZx39CRTr77+h02btrJuWc1ddYdGt0PvT+BCGsoS65q6iR81KV9L2VFzgr+sPYP3HIgEFxdcnfrK2EZjDoiEu0UZFShGxNy5MfS/rfWnXtK6FcUyHpIsoRf1dhcW8+YIBv6Djov+hWV/23JRVE1LhkRj7e+gZXX3MKQfZupueN+Rgzv36y8e/9+qhYfmZBP9GHpkMkUuHj36/cwUVHnAOe0Wi4u9jK27/gVdc49BDnSTmENT5wkSdwwqTdbDlfx8JI9TOoTTri9/YEciWEWnrlyBJP7RnLvRzupdft47qqRjUFLpMPE4pvHc8Prm7j5zS18tfAMQm3tL+Lbk4mApZPM/ULb3T+43b1doygKXq8Xj8fT5v2Ptxk8Xoa666i2R7K2ykm9olKvKNQrKp4O5mc5U03iXV81yjND0ZnsYLCAwRq415sbHydVF5LuCOOrrc8gS3LbNwL3Bl1g3Ru9pG9cF0cv63ErPvR6O+uqnDj0Mml2C8t2FWMKMfHaxYFAwWTUccu8YZTWuVG1wMVmxO4SHvhqP6vwM9flx5tfR0l2oNkouFfzEUAujwebyYgsy9RVVlCfOpQVm9PJKgvM3eCsrgJ7MJEWE9f+/k9IkoRmc2B1OYmQnY3fzvRDroacR/C8PAbptztx2XsRU7GhU7/HmLB4Sg5CVV3LzsFHBQUNQXGtoqHOidVh55BhPzqt7T/Lym0bKQqTuDY/uc0ymqLgzc8n/PrrGpsfqssLiP5iC/kz0xge3zx4io6fgE7TqKnIbH6i/C2BjrfBiS1eQ5IkJgx+m/V7rmHvmtmkDL6delc2+dm7cTmrsIWYmPCra1n6j030GjK92bHDhvTnG8lA5u49zQIWTdNQpDqs+tY7HloNVt447w32Vuxl1friNt//UUOmJfDlS7sJzwx08G6rgdbjV3j22zU0rP+O4bYhTO49CNXlY+6ObNZVO/n3oCTOCHNQ41OINxrIqagnq9RJVomTrNI6DpQ6OVhWj9evMC9jBdsuexlbdTnRDbXk3f0IZ9/U1Dm6IX0bFS+9hPO77zAkJBD76F/QhZ+cpQ9+yXQ6E3Zbf+pqd7VbLjR0IgZDGCUln//sApajHp49mGlPrOLtDbncMbNvi/3LdxWxdGcR2/OqGZ4Y+DIxZ1QCQWY9t7y9lX+tPsBtZzYdF2wx8K9rRnH202t46LPdPD+vs8vd9jwiYOmBdDodFosFi6Vr7ejz29juVVWciorTrzTe1ykqdf5AQFPviWCTtYrRBg/4XeBrAJ8rcPM6ob4MfA0oRjsbHcF8f/hLNE1DRUVV1cC91nTTNA2/5m+cSv7HGsIX4NH34dLtgWaNf/ZNoLLQyazJzReYvObVjWzNadmBzgZU/ncvAMnYWG+GJ557itTUVKKiooiKiqLB48NbVUlDbQ0FeflwZMG/w4cPB8596218tvhdylweXvnHEwxL7Y2lrgabpMesVLBt+/VIkoxk0EFaCMN2V6M+M5xBUjBGpYT/ezcds0F35CZjNugw6mSM+qabQZYIUvR4nOspK4tClg1IkgFJNiBLBmTZgNnqQPIq5OzbS1zvwD9YRfJT5a5ClmT0sh6dpAvcZB0haSMI3qCR5StE9XqRDIYWqW+lshJ8PkIuu6xx27pn/kC8AmPverTFz1MymIjUJErrf9RnqGx/oFlIbj33Zo2eAHug1FjKwNAz0SdeQ8bWC1C8oVz/t0AgssIWR1VR87loDHo9ruBY3IebN2tpmoYqNRBubv/LwaDwQXwWtQ25g1kEUoZF0mtQGLmLDzF0lBVlclPgfjSpse7AIT759FNszkBQEzs8FrVGY1+Dm3XVgRDnlr054FYwbi3H0KA0LtwYbjOSGmVndHIoc4ZE4X/6caYeWM+h5MGUDBhB0ry5nH3eFDRNo37dOipeepmGTZswpvYh7onHCTrvvMZmIaF9iuLC5S4gMurcdsvJsoHIyLMoL19F39T7T1HtTq5Ih4lpA6L4Zl9JqwHL099kklniZNbQWK4Y3fRl4uzBMdwytQ///PYA5w2Jpc8xg0Cig8z8+aLB3LF4O+cMLuTCDpqbeirx13IaMMoyYbJMmKGdX3fvjv+4I4E/dPG1NU1D0RT8qr/x5lJ8+GQHuW4fc7Zns3RTPpJGi1lFjqbh3/71OIx6GVkCS7mL3gYDxgQHqsuPUuPhou8kCkzV1Ehu9uzZw7p168BkRqqr5F83XY03JBJikzhr2lQSeqcQFBTEunXr8BnN4Kun0NmA+9MPCI1LJLdPFDP4Gp0uFDQFDQ0tdQYl/v1E6PuicyusLdVT7vTi9iu4fSoen4LLp+BTVDx+Fa9fxauoaBr8YVw8KcHL2LlrWZs/I8VnZuXr1aBtIGFgf/JDMjjjf2e0WlZCQrNLFOWVkjF0GEgSksmEZDIhG41IZjO+vECTVcnjT6ALDqbWrBK5dCPfjtDR33cAU04eRp0Ro86ISWfCqDPSgIarvhwqD4Ksh7riQLBqDQfFD630W1JdlaBp9LOch94e+Aeo+cIwBTX18UkePomMHz7kwJZ9pI5u6qxtS0jBk7Gl2flkWcakxZBbl9Pmz6rx56DTQGm/mUaSJc7/7VDeemkH52+twnuFAkf+BjQgr7qGTz76EE2nY8qcK9j44UcU5+aB3kKpz88fUpKw6GQiDHre2pHPVqef28/tz/heoaRG2RvT9bWFxay/5iaiiw9Tcss9nP//7d13eFTV1sDh3/RJ7z0hoYYSem8C0lQEQVFBBEWxouJV71WuvWK7iuXzKtiuoqAiKoINlCZSQiBIbyEkIb33qef7YyAYA6SQZCZhvc8zTzJnzjmzNic6a/bZe+37HbOyFLudkmX/JXfZaioPHsMYF0fk22/heeml1VbMFrXLzvkZm62UsNDJte7r6zuA9PQvsFgK0el8mzy2pjCxRzh3Lklga1Ieg9pV74GLC/fBqNPwf2fpKblvdEdW7k7ntTWHa7w+qWc4v+zL4vHv9jKgrT8h3i2vIrAkLKJJqVSqqltCp/me+llmc3xTHdY+gI3bT/LtphOklJlYcV1fys1Wovzd2Z9ezIC2/uhPrxMU87c3iPSid7fR9P7LJpPJRFZaGrayYqyVFeTn5fPjth0cSjpOZl4+BQUFpKenM3jwYMLDwwkOCCAwJOT8g/R6OX54AGNOPc5HOV2QzDoGnboSRbFgt1tO/bSe+mnGZrNQlmek+4Ph2GwKg8qfJc/zJFqDGptiw2a3OX7+5XdzQR7dA9wJH+yN3WRCMZlRTJWO3ysryf/kUxSzGex2zMnJlGYlU+gB3w6Egk2PnD1gtYq47KPwZu/q2wtPwLMBoDE4CsadfujcHQMhO6s4ad5B8f6HUKsNeEY6qt7++tk2DDp/fEIHolKvZdXCFxg0ZQIavR6tXo9PZR6lllI2Jh7lkl5nblHZVRVoqH0VY5UGFEvtgyo1WjWecX6U7SnAbLaBx5lJ4+/9vBad2cQNt84hLjyU0t/asy/7CEF+0Ri1Gu6NPlPccIMqkwTgjmFtMWrPDHovzS1gz+Rr8TKbsP3n/xh5+SUoFgvF/3ud3I+XYM614B4GUR+8j8eQIS1qIKgr2b//QQDSM5aTl7ee8PBpREbccNZ9fX0cH9RFRTsJDLy02WJsTOO6htArypdHvv6T7+8dhpfRcWs3raCcVXsyuHtk+7MeZ9RpmD20LQt+OEB2SSXBXmeSEpVKxbOT4xi/cCPPrNp/1oTH1UnCIpym9NS070s6BDLy4ZGMe2k92afKSnd94mcAZg+NOZOs1JHBYKBN+zP/QdtsNjLNNoqLiykpKUGr1TJjxgzatz/7f/SNQaVSodOo0GkMwPnvXfj95Q5IJH7AhU0bD37wwWrPYwCzzcwYtY5KWyVmmxmzzYzJZqr6aS9KpavaAxQb2K1gtzmSFa3B0R1hKXf0uJjLq37Xm8tpn7KGgo6BVFSkYreb0OtCqCw1UJyVi6kErGYbOvex2My/suvnVdgsFqxmE7ZTSygs/u1AtYQFuxG76vxVpQHQAJV1+/BXn0oS/jq2VUHBdqoicJfQYI5s2sv+vCTcNQZOmi2YNVosdgWzYsdiVzie64gps7ASs83Ri2ay2tFfMQJ/mwXzPQ/RzmAj/60XyF/yOZYiG55tdYRd0QZ3SzwM7Hf2AnyiVoqioFYbsNtNJCf/HwCHDj1OcNB49Pqa43+Mxij0+iAKW3DColarWHh9L65863ce/vpP3preh5MFFTzwZSIBHnpuO8+q1FP7RPLSTwdZmZheY/Vqfw89l8eFsnL3eeoruTBJWITTlJ2azump0bAyyVH/Ynrf6qvyzhwUXeO4+tJoNFx11VUXfJ6WTK9x3HBz07rhpj3L2KiAhiVJMdTs9AKg2iLSQ/j7CCvFbuffK3aTk1a9PkSke2dSynejKMp5eyNUWlDOcUtIsStYLXasZhsWsw1bsWMc1faiMg4rVsyKgurgPgzHHLPxFj77KiVKOaGKL+PUfaEQNgVB1IYz1W6Nu7MAGPmXsgVe5jK+tDnOrX/7VdJOb++gJfLphzCOvwVSt8OH4yBzD0RWn8km6kalUjFq5P6q55WV6fyxZRRZ2T8QFTkTu7kMW0kqdmsZirUcu7WCAEsA5Zm/Q/uHnBj5hYkJ9ODVa3tw55KdrDv4MxUWG95GLR/e3B8Pw7k/un3cdQxuF8D6Qzk1EhYAL6OWcpON3FITgbXMQnI1F5SwLFiwgH//+9/MmzePhQsXYrFYeOyxx/jhhx9ISkrCx8eHMWPG8OKLLxIefv5BPl9//TWPP/44x44do3379jz//PNMmTLlQsITLq7sVA+Lh0bNZztS0XnruauXYxDZkPYB/HEsr9ZpfaJlUqnV+Hm6UVJZQHZJJUXlFoorLUQb+5FsXsu/1r1AlK+XozfIbq7eK2Q3o69sQ3RxD758Ib4qMbGa7VhNNqyWmvOBbCq4/0gaFp0jySlyO3PbqUQpx8vuxtUdx6D3daPETYNXiI43At3Qq1XoVCoyg4NIPVHE+NhgDFoNhlMDq40zuhAa6I3KaESl0aD6cBSaPtfAmFPVhSP6OsYB7f9OEpZGYjSG4+c3mLzc34iKnEn5O53xLKy+UGpXwKrVwGD7OQeNtwSXxYXx/T3D2JqUR4iPkbFdQnDT177W0LAOgbz6yyGsNjvavy3IO2dYO/73xwkWb0pi/uUX1pvb3BqcsMTHx7No0SJ69Dizgm15eTk7d+7k8ccfp2fPnhQUFHD//fczadIkduzYcc5zbdmyheuvv55nn32WKVOm8M0333Ddddfx+++/M3DgwIaGKFxcqfVMwlJcbiHAz4harabcbOWPY3nERXjj49a6l0u/mPm66zhZWMGA53/9y1Zv3KKj2ZzxC175xmqDg3VqXdUgYUPnSjy9FYICvdDpNWj1anQGDVqdxvFTr0ar16DTa9Do1eQZ4Qp/I+5qNXq1Ck9Nd/RXj0N9lg8zX6DGRO5gX+h2llWVQ7pXf661g/ovf7MaLXS7Gv54E7zCYPDdDfq3EtVptd6YTFmYi4/jUVhMTuce6GOvQaU1otK4ocs6jHHD21CWDV6hzg73gnSP9KF7ZO0Lhf5VXIQPJqud5LwyOgRXLxPg56FnxqA2fL4thfsu7Xje3hpX06BIS0tLmTFjBosXL+a5585Mk/Tx8WHNmjXV9n3rrbcYMGAAKSkptGnT5qznW7hwIWPHjmX+fMdMlfnz57NhwwYWLlzI0qVLz3rM6VokpxUXF591P+G6ymw29Ke6/U12OyUninlq5b6qhfFuv6TpxpgI57u2bxQBHga83XT4uuvwcTv9uKJeK9bWRbNN4ixOg+z91bf1nwPxi+Hn+dBnFhgavuaYgJL0dRTlbiY4fDIVBz5DD3iN/A/G0AEA2G1WPt0/nRURYczc9h+mjnnFuQE7gZ+HI2kuqbSe9fVZg2N4f9NxvtqRys1D2zZnaBekQX1lc+fOZcKECYwZU9tcCce6LSqVCl9f33Pus2XLFsaNG1dt2/jx4x3TU89hwYIF+Pj4VD2iomoWtxKurcxmx6wodNi4hwqd409x4+EcInzdeGxCFy7r1rK/GYnz8/PQc03fSMZ2DaF/jD+dQrwI8TY2erLS7I6tq/7cLxranpqmfvz8q4aLWhSdxGvRZLomnqBNmzkoh36kwt2AIaR/1S5vfHs9/yk9QJJexytZG4jPjHdiwM5hODWTzWQ9e7nECF83JnQP44PNx6vqCrUE9e5hWbZsGTt37iQ+vvY/gsrKSh555BFuuOEGvM+zwFdmZiYhISHVtoWEhJCZee5qlvPnz+eBBx6oel5cXCxJSwszOsCb1EozsR5GuvZoR6ybAX+3lls2Wgg8Qxw9Kqelbodv73bMuBr+EHQ6e0l5UQeKAt/NBcC/0IKiC0STcoCiTn1wO9VT+8XP9/Fh6WH+GTyUqWNf5/5193Pnmjv575j/MiBsgDOjb1aGUzMrz5WwANwyrC2T/y+ddQezGdM15Jz7uZJ69bCkpqYyb948lixZgtF4/qIzFouFadOmYbfbeeedd2o9999nBNQ2S8BgMODt7V3tIVqWrp5uvBwbxa2RQQz295JkRbQCKhSbGVNBFhVf3wcfjHOsxXTHJhj9OKhbeO+RM8W/D0nrKO93LQCm16LRWW3oOk3CYqng7W+m8ULGb9zoFsOsK97DXefO26PfpmdwTx7f/DjllnInN6D5nC4FYT5PwtIrypeekT58srX2Qo2uol4JS0JCAtnZ2fTt2xetVotWq2XDhg28+eabaLVabKemqVosFq677jqOHz/OmjVrak0mQkNDa/SmZGdn1+h1EUIIV1ZWWoZq4ysY3uiEbvcnVAx6CG79BYI7Ozu0lq04HdY8Cf1uxe2KRRT0uxKbTku5hxsFXp24eclQ3i/ay91+vXjo6q+rDtNr9Dwz5BkKTAW8sfON87xB63Kmh+X8K4HPGBjNpiM5ZBRVNEdYF6xeCcvo0aPZs2cPiYmJVY9+/foxY8YMEhMT0Wg0VcnKkSNHWLt2LQF1WNhr8ODBNQbr/vLLLwwZMqR+rRFCCCdak9+LnfrxpMTew8dJfUksay+9Ko1h7VOOxVhHP+GYEn/lZ6hv2crUwGAm/v5P8hULn/R7jDuuWoJGW72nNtIrkvt638fSg0vZlb3LOfE3s9NjWM7XwwJwWfdQ9Bo1K3aebI6wLli9xrB4eXkRF1d9BUwPDw8CAgKIi4vDarUydepUdu7cyapVqxwVRk/1nPj7+6PXO/6QZs2aRUREBAsWLABg3rx5XHLJJbz00ktcddVVfPfdd6xdu5bff/+9MdoohBDNIssSQHDHcbS5dgZhOa/wx5efsfPH7xk581a6jRjt7PBaptTt8OcXMPFNcPN1bKss4qcVN5CqVhiEkYXXrsTDK+ycp5jeeTo/Jf/EU388xYpJK9C08iRSpzk1+7KWhMXbqGNiz3A+35bCnSPao1G7djXmRq2ok5aWxsqVK0lLS6NXr16EhYVVPf464yclJYWMjIyq50OGDGHZsmV89NFH9OjRg48//pgvvvhCarAIIVqWv9T/HzD5WoJj2lNZUsxP77xOZVmpEwNroex2+PFfjno3vW+EikLYvQwWjURbmALAa9N/O2+yAqBRa3hkwCMkFSWxJmXNefdtDVQqFQatGpPl/LeEAKYPaMPJwgr+OJbbDJFdmAuuGLN+/fqq32NiYlCU2qdI/fWY06ZOncrUqVMvNBwhhHAyx7fUoDYxzHzpDTYs+ZADm9ZhcK99UUfxN0umQPqp2zivdICKfMfvQZ3pNWUJ+vX38uHeD5nXZ16tp4oLjGNI+BA+3PMhl8Vc1oRBuwaDVo3Zdv4eFoA+bXxpH+TBVzvSGN4xqBkia7iWW7NYCCFczNm+rmUePUxYx86gQNrBfOwtqO6F0yWtP/N7Rb6javCdm2HuNqKihjC101RWHluJXan9gxkct4YO5B/gYP7BponXhei1GkxnWabi71QqFdf1i+KnfZkUlVuaIbKGk4RFCCEaSVlBfrXnFlMl6YcPEtWtByn78/luYSK/LN7rpOhaoKeK4Il8x89Hs+DajyD0zDjKMdFjyC7PZmvG1jqdbljEMHwNviz6c1FTRewyDFp1rWNYTpvSOwKrzc7qPRm17+xELWcRASGEcGF2u2O8wJbln5OyL5Og6LagVGK3WSkvDmbL247Vn611/BARp5weIKurWfurT3Af+ob05d+b/s0XV35BiMf5S2EkFSZRZCpiQ+oGzDZz1SrmrZFGrcJehyEaAMHeRoa0D2T1nnRuGHj2JXRcgfSwCCFEI1ChQufeF5UmhIzDO0j86QMSf/4MVJ4k/uYYcKtSwZibuzo50tZDo9bw6ohX0aq1XLfqOsosZefc12w1M/vn2Y7f7WbWp65vniCdRDnrDcpzu6J7GJuP5rFw7eEmiujCScIihBCNQKVW4xU0luEzHuMfn3/OnYuWcu0TC7n63y8RHO2Nm5eOGc8MxughK5A3pkC3QN4Y9Qb5lfnc/NPN59xv7m9zKTYX84++/6BHUA++Pfpts8XoDIriSJDralw3R+/UwrVHXHYsiyQsQgjRBDx8vIjs3I5DW4opyChjwt098Qlyc3ZYrVK3wG70CurFwfyDHMg7ULX9eNFxThSdYNnBZWzN2Erv4N7MjpvN5A6T2Zy+mayyLCdG3bQUxdHrV1eBngZW3TsMgG3H85oqrAsiCYsQQjQBs9XGZ5/s4/juXMbN6UZIW1nvrLHY7DYUReG7o99x99q7OZB3gPfHvw/Az8k/A1BqLmXSt5O48tsreX7b8+jUOhaNdQy2vSzmMvRqPd8nfe+0NjSH+t4WiovwIdLPjS1JrpmwyKBbIYRoZJUWG9027KEsBjb16kbbnq5d36IleWbLM3x1+Cu0ai1WuxWAzembeXXEq0R7R1dt+2DPB9WO89R5YtQ6Bu566b0YEz2Gb49+y61xt553od2WSq9VY7XVfwr94HYBbDnmmgmL9LAIIUQjMtnsTPlxD2WnJrd07B181v1ycn9l95+3Y7EUN2N0Ld+vKb8S6BZYlZh08O3A2OixPLD+AU4Un0Cv0fN6wuu8v9fR47J0wlKeGPQE303+rtp5pnSYwoniEyTmJDZ3E5qFTqOq87TmvxrSIYCDmSXklZqaIKoLIwmLEEI0kgqrjVsOJbO73LH67YQgn7Pul5L6EX/+eTu5ub+SmfVtM0bYsn156EvyK/N5bOBjrJy8kgXDF/DJ5Z/w1OCnqvYZGjGUD/d+SJiHo1z/9NXTSchOwFtf/ZZcv9B+BLsH8+uJX5uzCc1Gr1VjqUOl278b3C4QgK1J+bXs2fwkYRFCiEbyraoCc14lmqwK1MBgX88a+2Rn/8yRIy8QGXkTAQGjyMpa3fyBtkDxmfE8v+15xkaPZVjkMNr6tOXKdlfipffCU+/J8onLeWHYC/xv3/8AmNl1Jk8PeRqA1UmryausfptDrVLTO7g3e3L3NHtbmoNO07CEJdTHSNtAD7Ykud7aQjKGRQghGkmaxlE8TpNRgd2rBJ8u1VcFzshYwf4D/8TffzjeXnEUFe3EYil0QqQti8Vm4fHNj9M3pC8vX/IyWnXNj65Y/1hi/WM5UngEUqF7YHd6BfdCp9bx79//jWIqZeqy0RzSwASdY0zRz5YcABS7HZW6dX1/dyQsDVsGon+MHzuSCxo5ogsnCYsQQjQSd28D5afu/atzTbydkk2ZuYThlm/Q6305fORZ/PyGgyWS/Qf+icEQStu29zk5atf3+cHPySjL4K1L3zprsnLa1oytfLT3I2K8Ywhxd9QVya3IxU3rxks/3cGhU/njaksOfRU9fWxqoiqKwVwKxtY1i0uvqdvih2fTL9qfrxLSKK604G10nbpBkrAIIUQjmtA7nNW70rFFuHOorJJHjlUyQ0nmChxTaAsKNgGgKryDYVf/y5mhtghJhUm8t/s9ru54NR39Op53313Zu/DSebFy8sqqmT/l1nIqrBWsoYK7LQZC249n7IB5eHoEw5G18Nk1YCppfQmLVo25gctA9In2Q1EgMaWQSzq5zgw3SViEEKKR5JaaWL0rHQWIaeNNktUxk2UPPbnMugYq++Hl2ZuUnRH4BUiJ/tpUWit5YP0DBLsH80DfB2rdP8AYQJm1DKtiRady9AxM7TiVrMzdDDq2hSs0bjDq+TMHnE5SKovAJ6IpmuA0DZ0lBNA+yAM/dx07ThRIwiKEEK2ZPcRIhJeRK7zc0ezKIjw1nLH/2Ff1euq2jVgqipwYYctwx5o7OFZ0jG8mfYOX3qvW/UPcQ7ArdnLKcwj3DHdsMwbwzNYvHDvMeqv6AYZT5zSVNGbYLkGnUVNqsjboWJVKRd9oPxJOuNZModY1ykgIIZzozem9+WzOQE7Mu5SvenXgsfbh9MrV4q8NrdqnILOAlMSXObjpJSdG6vpOlp5kZ/ZORkSOoINfhzod0y2wGwB/5v55ZqNGC1e86vh9+S2w79u/vHZqtWa7a66dcyH0GjUWa8MG3YLjtlBiSiHWBo6DaQqSsAghRCOZ1DOcoR0CUavPVE5NSswhZZ9jSu3xxCN89MA9ACh2m1NibClOTzc+3VNSF0WmIgLdAtmdvbv6CwNug6H3Q3kefHUTFKY6tp8ewGtrhQmLVo3pApKNvm38KDPbOJxV2ohRXRhJWIQQohmkHkjmm5ceQ61W0aZ7PwKjop0dkku7LOYyJrSbwC/Jv1Bhrah1/4zSDCZ/N5ncilzWnFiD2WauvsPYp+G+RABybxvE4Z6dSbryMo7/HIj5ZGYTtMC5dBo1lr+NYckrNXEkq263v7qGO8b3HMhwnUrMkrAIIUQT6jLEDUvZGr556RlUai03v7aQkHYxWMyuV/rc1cztOZdCUyFfHvqy1n3/uh5QVnkWq5JW1dzJvy1lmsHk7PHGZlJh7BBDZYEeU+XZKxK3ZDqNmqIKC0u2nuCBLxMZ+co6+j63lnELN5JfZq71eC+jjnAfI0dzpIdFCCEuCj6BZdjMe7BUZBMY1R3f4EC0Oj1Wc+0fGhe7KO8oJrWfxId7P6TcUn7WfdacWMP0VdN5LeG1att9Db419s37+GNSPjuBsY0/0a8/RvCrnzhe0Lk1duhO52XUcrKwgidX7uNodikjY4O5Y0Q7FAXK6jgYNzbUi4Mu1MMis4SEEKIJdejXnfWOavG0iXMMCtXq9Vilh6VObu9xO98f+54vD33JzXE313h988nN7M3by968vVXbXhj2AqOiRtXY13TkCAAxP25EpdFgzXFUuqXhY1Nd1pzhbbmkUxBdw7xx0zsq5sUn5/PehqQ6T3fuGu7N8oS0pgyzXqSHRQghmpBW75iJ0mnQeIZPvwIAncEgPSx1FOkVyVUdruKjfR+dtZel0FTI0PChVQnKLXG3cEXbK1CpVNgKCzGnpABQvmMHJT/+hMpgAPupD+y/3EZqbbyMOvpG+1UlK39V12Z3DfMhq9hErous3CwJixBCNKHUfY4ptqNunoFa4/jw0OoN2CwWFLvrTBl1Zbf1uI1iUzHLDi2r8VqhqRAfgw+vjXyND8d/yH2970Ojdvw7H5s4kWPjxmNJT+fEjTOxl5cT/tJLqHSOonL2SscH8ennrZ3d7uhKUtcxY3G1gbeSsAghRBNK2fcn/hFRePr5V2073etitUgvS11EeEYwueNkPt77cVUvy4/Hf2TRn4tIyErAz+iHVq2lf2j/qmSlaNVqbDmOFYePXjoagOjPluB92fiq81oz0gHQRdR96nRLdipfQV3HHpZof3fc9Rr2p7tGwiJjWIQQogml7d9Dm+69q22rSljMZnQGozPCanFu7347q46t4tKvLkWNmhLLmem57Xza1TxAqT4wJeKNN3Dv27faNvPJkwDowi+OhEVR6tfDolar6BLmzX4X6WGRhEUIIZqQRqujoqT6//C1egMAFpMJt9orzgsgzDOMD8d/yO/pv+OmccND78HYNmPx1HuedQVn7ysnkPvf/6Jv0wbf66/Da5RjjIu1sJCT9/8D1GrcundHExiI2nhxJI22UwlLfYbudA3zZtvxvCaKqH4kYRFCiCYUN2oc6z9ZzKHhlxLbtz9QvYdF1F33oO50D+pep31VKhU+UyaT85/XCJhza9X2vHffpXzrVgDKt27FcGrm1sXgzC2humcsnUK9+Hx7ClabHa3GuaNIZAyLEEI0ofWfLAZg1ctPU1yQz+r/fcCKNxxr2xQVFjgztFZPH+FYgdlWWEjxL7+Q8/bblG1xJCs+kyc7ZguZLp6k0V7PW0IAkb5u2OwKOS4wU0h6WIQQookcP3Sw2vPFd85CAbR+gVSEt0Xl7umcwC4CitXKyQcfAsB98GAO9+1X7fWwBS9Q8ssveAwd6ozwnOLMGJa6HxPq47hdll5YSZiPcwvsSQ+LEEI0kf8tXUZ5m07MfO1djKGRRA0dxQ0vvcnNLy7E6hOA1Vq3iqOi/swpqaAo+M+ezdFRl1Z7zf/WW7CkpGAvL8dj8GAnRdj8zpSfqXvGEn4qScksqmyKkOpFeliEEKKJhIWFkZEBvkHBzH3jXRRFoaK4DPupWl4WS+tbJdhVaHwd6wPpoqOxFxej9vAg+otlGDt0ACD7tddBp8N4UY1hqX8Pi7ebFqNOTWax8xOWC+phWbBgASqVivvvv79q24oVKxg/fjyBgYGoVCoSExPrdK6FCxcSGxuLm5sbUVFR/OMf/6Cy0vn/QEII0VBTp04FYPeORCpLKvjgxXd4+fVXWfjq6wBUltW+CrFoGI2vL2g0ZD31FAAqvb4qWSnd/Sd5H3xA0N13ofXzc16QzSzY23F758UfD2KuY3l+lUqFh15LQR0WTGxqDU5Y4uPjWbRoET169Ki2vaysjKFDh/Liiy/W+VyfffYZjzzyCE8++SQHDhzggw8+4IsvvmD+/PkNDU8IIZwuICAAH09vVv/yA+++9jZpphw6eUfTM6wLAOocuSXUVFRqNdhsVc9Pj1UxV1Sy465/kBUUhf+tt57r8FapV5Qvr13Xk+8S05n14TYKy+uWhIzoFMSPezOaOLraNShhKS0tZcaMGSxevBi/v2WnM2fO5IknnmDMmDF1Pt+WLVsYOnQoN9xwAzExMYwbN47p06ezY8eOhoQnhBAuY6hfTwAKlTIAfLy8mXjHVG62jqK9T6QzQ7toxKz4mohXXwFgzb+ew78gi6e6XMP6pItvltbVfSJZMmcghzJLuPqdP0jOLav1mH4x/iTlllUN2nWWBiUsc+fOZcKECfVKSs5n2LBhJCQksH37dgCSkpL44YcfmDBhwjmPMZlMFBcXV3sIIYSrCT+iZ6ppUNVz46kiZTqdHsViO9dh4gL99cPVrWtXAPb++gfRa1aQdMU0wvvE8dyqA3W+NdKaDGjrzzd3DyUpt4zbPqm9Y8DTqEVRoNzs3L/Xeg+6XbZsGTt37iQ+Pr7Rgpg2bRo5OTkMGzYMRVGwWq3cddddPPLII+c8ZsGCBTz99NONFoMQQjQ25VSlLl/Fg6n9J5CwP5FBEy4BQKVTo1guvg/L5qJSqfCdPo2KXYkAVJSVk/PYY1iC2nD58/+ic2ElV7yxif+uP8a8MR2dG6wTZJc46qpc1y+q1n29DI5UoaTSiofBeXN16tXDkpqayrx581iyZEnVt4TGsH79ep5//nneeecddu7cyYoVK1i1ahXPPvvsOY+ZP38+RUVFVY/U1NRGi0cIIRqDSq0i9JEBAIRkuXHTP2/Dw99Ri1+tU2OXhKVJaYOCsObkALD2X88RUJRN5IsLMBj1dA71Zu6oDrz12xGXWdyvuVhtdq57bwu92/hyy7C2te7vZTydsDh3Vlu9EpaEhASys7Pp27cvWq0WrVbLhg0bePPNN9FqtdhsDesuevzxx5k5cyZz5syhe/fuTJkyhRdeeIEFCxZgP8fy6waDAW9v72oPIYRwNVpfA55DwzEdLqjqcYFTPSz17GI/duwYe/fubewQWy1dcDC2vDy+nPs4bX/9lhMTptN16JmFKO+9tCMdgj25+aPt7D1Z5MRIm09uqYkp7/wBwM1DYtDUYY6zt5sOgGInJyz16tsZPXo0e/bsqbZt9uzZdO7cmYcffhiNRtOgIMrLy1Grq+dOGo0GRVGcPshHCCEulKGtD6Wb07Fml6ML9QBApdOc9ZaQzWajoKCAvLw88vLyyM3Nrfq9tLQUgK5du9b4f6aozmKzsy7XTmeg+6/LSRs7hcuf/1e1ffRaNZ/eOpA5n+zg2ne3MKFHGHeOaE+H4NZZgXjLsTzu/2IXNrvCkxO7clWviDodd7qHpbjSubPa6pWweHl5ERcXV22bh4cHAQEBVdvz8/NJSUkhPT0dgEOHDgEQGhpKaGgoALNmzSIiIoIFCxYAMHHiRF577TV69+7NwIEDOXr0KI8//jiTJk1qcBIkhBCuQt/WB22gG7+8+y3xHGFE7CBsRSXo1O64xxdRUFBQlZgUFBRU9SzrdDoCAgIICAggJiaGjIwMDh8+zO7du+ndu3ct73rxik/O598r9pCSqeeJsdMYffeNdOnS/qz7BnkZWHbbIPo/v5blCWmUmaz898a+zRxx00ovrODVnw+xYtdJBrb1583pvQnxrvuwDm+jo4elpCUlLHWxcuVKZs+eXfV82rRpADz55JM8daqAT0pKSrVvB4899hgqlYrHHnuMkydPEhQUxMSJE3n++ecbOzwhhGh2Gg8dwXN7Ef/SKgA2HNqKSgElC1Q/qPD19cXLx4eOHTtWJSiBgYF4eXlVK6Nut9t5/vnnyczMdFZTXFqF2cYrPx/ioz+O0yvKl6/njSIuYnKtx7npNUwfEMXiTcf5cW/L/7e12RUqLDYWb0zijV+PABDoqWfB1d25vl8U6vqUugXc9Ro0ahXFFc69JaRSWsk9l+LiYnx8fCgqKpLxLEIIl2S329m6dStr164lNDSUa665Bj8/P1bs3sfe777Gs98gBsV2ZFjHs/cGHD58mM8//5xp06bRuXPnZo7etcUn5/Ov5X+SXljBQ+NiuWVY2zqNzzhNURRu/zSBNfuzGNEpiBeu7k6Er3MX+2uIpJxSHv76T+KTHTVmekX5Mq5bCLMGx+B5ATN8ej79C3eMaMfdIzs0VqhV6vr5LWsJCSFEM1Gr1QwZMoTo6GiWL1/Oe++9x8SJE0nYmYABKN2xlbU7tuJ56+3EhQXzxx9/cOzYMby9vbnmmmv4/PPP0Wq1xMbGOrspLuWTLck88d0++rTx5f2b+tE+qP5jUFQqFYtm9uWt347y2prDDH3xNxKfGIuvu74JIm4cFpud/DIzPm46Viams3hTEsl5ZQR5GhjRKYh/XRZLt3CfRnkvbzdt67slJIQQ4vwiIiK44447WLVqFV9//TValQpTx65MvXQEy99fzNebNhMwfBC//fZb1TEjR44EYNSoUfVabfdikFfqKDH/7o19q9bLaQiVSsV9oztittpZtDGJ4S+t49bhbbllWNuqcRzOdiSrhJ/2ZrL2QBbJeeUU/e02zcOXdWb20BiMusYd/+ll0Dl9WrMkLEII4QRGo5FrrrmGgOho1v74E+t1Hszx8SGoVx+KE7azOCMNNXDVVVfx3XffVRXr1Olc44PTlcweGsMHvx9n6fbURikC99D4WG4aEsO7G47x3/XH+GhzMrdf0o6bh8Q0e+E0RVHYcDgHs9XOlzvSWHsgq+q1OcPa0jPKl8IKC1abnWEdAukY4tUkcUgPixBCXMRUKhWj+vfHu0Nnvtt3gocPpfHVlZfzH3dPSjY5eld0gUF4e3uzdetWABmjdxa+7nq6R/iwek8691zaoV5jV84lyMvA41d25fZL2vF/646ycO1hXvn5ED2jfFl+52B0mqafVp6YWsizq/aTcMIxHiXSz41XpvZgdJcQPAwaDNrmm0XrZdQ5fdCtJCxCCOFkff28eDU2ipv3HmdzYSkPjb6E/f36cN2GHQx392bOnDmkpqaSsuEgXquKKDfn4NbFH1Ujd/u3VIqikFNqIiWvnNJKKz7ujdcLFeJt5Jmr4rj9knYMe2kdu1MLGfLib1zTJ5LRXYLp08avURKkv8orNfH8DwdYsfMknUO9ePuG3vh76BkQ44+2GRKlszFo1VS0tLWEhBBCNL7xgd708nLnxaQMvu/jSbSHG7levhRbbXgH+NGtWzeCtymYUgrI//wg7r2C8J8mM4UAftiTydHsUt69sU+jJit/FennzvEFV7A/o5jPtqXw5Y5U3t1wDKNOTWyoN+2DPGgb4EHbIA/aBjoe7vr6f8Qm55Zxw+KtVFhsvDClO9f3j2r0hKghtGoV1nNUnm+2GJz67kIIIQDH7aFH2oUybXcSa/OKGRPgjRootjq+1Vqtpag91WiD3bFml1OemIPf1E6otFLx9rNtJxjUzp/L4sKa9H1UKhXdwn14YUp3nr0qjt1phew8UcD+jGKSc8tYdzCbgvIzt008DVrHw6jF102Hr7uOYG8jNw6Mpmt4zVt7BzOLmfXBdjyNWr6+ewhhPq4zrVqjVmO1ObcKiiQsQgjhIkb4eTHIx4OXj2cSaE/DDmw89DaRhz4FRUX7PW/gNSAaja8B0+ECzCnFGNr5OjtspzqWU8q24/ncNDimWd9Xo1bRp40ffdr4VdteUGbmeF4Zybll5JWaKTFZKam0UFRhobjCwoZDOSzbnsLzU7ozfUCbquNKKi3M/GA7QZ4G/nfLAIK8DM3antrotSosNulhEUIIweleljAm7zrK5fsc2zpyGABtZQBaize2kDz8LxlE/pID5Czeg991sXj0DnZi1M716ZYTeBu13D/2wmcHNQY/Dz1+HvoaicxpVpudZ1btZ/6KPRRVWLhzhKNI4KPf7CWnxMSXdwx2uWQFwKDVUOnk1cWlL1EIIVzIIF9PRvmfmZp6Xc+HGTZsG+2N8wHw6RSH1sdA0J090UV4UrIuxVmhuoTDWSX0iPR1mToptdFq1Dw9qRt92vjy4o8HMVvtfL4thZW7Hevvhfk0vI5MU6kw2/j4j2RKTc6d1iwJixBCuJh/tT0zFsOmCcCgD6QyqQCLdx4G3wAArPkVWE6W4jGgacdtuLLU/HISThQwuH2As0OpF5VKxYKrewBw92cJPPX9Pm4Y2IbjC65o9IJvjSG31ATA2K4hTo1DEhYhhHAxvb3d0ascAxzd1QqKoqDJ8EEddeYbbsm6VNReejwHXpwJi6IoPPrtXgI89MwcFO3scOotNtSLe0Z1YO2BbDoEefL4hK4uW8HYdGrg94Qezv1bkzEsQgjhgn7tFcjbu97GXtSF0tIh6MqD0J9aE9GaV0F5YjY+V7RDpbs4v3eu3J3OxsM5vD+rX7NXn20sD47rxLhuIbQP8sRN73o9K6eZrI6xKwYnz0hrmVdZCCFauY6+UdwebCEl9SNU5T5AIL7dugNQvC4VtbsOjwGhzg3SSQrLzTy7aj+Xx4Uyxsm3KS6ESqWiR6Svs8Oo1emERe/khOXiTM2FEKIFaNNmDpWVaRQdOIjFOwe9jy9lu7Ip35GF9+g2qF34W3lTevHHg5gsdp6a1M3ZoVwUzFU9LM79e5OERQghXJSXV1f8/Ybic3IYGo2R9MPHSVmxC/e+IXgMujjHrmw4nMOy+FT+dVksIRewMrOoO7P0sAghhKhNVNgcAIqLYfyWScyOeJCsoZUoJpOTI2t+O1MKuOnD7bQP8mDGwJY30LalcpUxLJKwCCGEC/MPHoYqRMU/Yl6m+3E7Hy20waXXc2TkKDKefhpzcrKzQ2w2vx7IAuDj2QNQu8D6OhcL6WERQghRK7VazdKum8jU59E9YmjVdnthIYXLlpL99D/h2Dp4IRLeHQ7vj4Gcw06MuOlsPprHhO5hRPm7OzuUi0qpyYJKBW5OrhEjCYsQQri4f4z9FwC/eR6t2qZSK3S5PoPI6F/g08lgLgGDF6TFQ+pWJ0XadArLzfyZVsglnQKdHcpFJzmvnHAfN3Qa6WERQghxHhq1hkVjF+GuPrPGjC7ob70M05bCZQscvwd1acbomsfmo3nYFRjeMcjZobRoFWYbR7NLeGf9UWIeWc2IV9bx/qYkrOdZ2DA5t4yYQOf3akkdFiGEaAEGhw/m2+t+IIVPKH/yRfTafACsocPR3vgReAbB7mWOnYM7OzHSprHxcA4dgj0J93VzdigtRoXZhoJCemEFW5Py2X48v2rNIrUKOgR70inEkwU/HmRfejGvXdezRrXdCrON7cfzmdI7whlNqEYSFiGEaCFUKhXR19+EeehoTF8+A8VfoM3cBAvjoN0oMBWDbxvHraFWRFEUNh3JIcTHyPKENHzddAR5GegR6eOy5eydZe/JIhZtTCI+OZ+s4koA7I5VHogOcOfq3hF0j/Rhcq8I/Dz0AHy/O517l+5icLsArusfVe18aw9kkVdmZoYLLH8gCYsQQrQw+shI9A8sAhZB3jE49AP8/jqU50Gny5wdXpMI8jZyMKOEh77aXbVt6W2DWtzCh00hr9TEkq0pfJt4kuO5ZYR6G4nwc+O+0R3JLjbx+trDPHNVN2YNjjnr8RN7hrP2QBaPfbsXBYXr+7epeu2X/VnERXjTNtCjmVpzbpKwCCFESxbQHobcC/u+cSQswV2dHVGjU6lUfDfXMUPKZLVxJKuUK9/6HfN5xl20RmarnZT8cp5fvZ+2gZ50j/Tm571ZrD2QhdWu4G3U8t8ZfRjbNQTtXwbIzhvTsdZzPzMpjqIKCw9/vYdOIV70buOH1WZn4+EcbhoS04StqjtJWIQQojUozXH8bIUJy18ZtBp83HQAaC6C20EFZWYeWfEnBzNLSCuowHbq/s66Q47rHRfhzaMTujC0QyAhXkZ83HUNeh8fdx03DY5h/aGcqn/fxNRCiiosjIp1jYHOkrAIIURrUJTi+Bnc+mYI/d3pD21NKy8etz+9mPuW7eJodimjYoO4/ZJ2tAv0pH2wB37uekorrVXjUBpDfHI+gZ76qts/m4/m4eOmc5kFGiVhEUKI1iSw9u7/ls6mtO6EJbOokld+PsSKXWm0DfBg0cy+jOtWc2XuxkxWwJGwDGjrXzWQ+WhOKbEhXi7z7ywJixBCtCZaQ6275Jaa+H53OrMGx7jMh1F9tOYelk1HcrhryU5KTVaenRzHtP5RzVKwrdJiY3dqEf++4syU+KScUrpH+DT5e9eVJCxCCNGafHo1DJ4LHUaf9eV/Ld/NlzvSAAj3dWP8Wb65u7rWmLCkF1bw2Ld7+e1gNn3a+PLs5Di6hTdfspCYWojZZqd/W38A7HaFpJwyJvdyfv2V06TSrRBCtHRHfz3z+7FfYcnVkLS+alOZycrGwznM+d+OqmTFXa9heUJaMwfaOE4nLE+t3OfkSBrGYrPz9Pf7+P5UEbffj+Qy7vWN7Esv4t0b+/D1XUOaNVkBiD+ej5dRS+dQbwAyiyupsNhoF+T86cynScIihBAt3YHvz/w+bzfoPSE1vmrTm78dYdaH21l7arXjI89fzqWdg0kvrGjuSBtFxxBP/D307E8vdnYo9Wax2blv6S4+2pzMd4knAXhi5V66hnvzyz9GcFlcmFOK4W1Pzqd/jH9Vr9XR7FIA2gV5Nnss5yK3hIQQoqXL/NPxM7wP+MVAQAcoTK56ubTSSqi3kZen9iDCz7GInb+HvupDqaUxaDXcPbI9C9cecXYo52SxOWqmpOSVcyKvjBP55aTml3Mku5T0wgriIrxJyS+nuNJCUk4Z80Z3rJpO3NysNjsJJwq499IzA7a3JOUR4KEn2oVWxr6gHpYFCxagUqm4//77q7atWLGC8ePHExgYiEqlIjExsU7nKiwsZO7cuYSFhWE0GunSpQs//PDDhYQnhBAXh8JUx88+Mx0/847CriVQ4uhRKSg30z7Yg0s6BdH+1Ddmq11p8WNAlFOzhZytzGTli/gUnlu1n1s/jmfUq+vp/PhPjP7PBmZ/HM8LPxxkw+EcbHaFUbHBfHLLQCb3iiA1v4If/swAICbAebde9qUXU262MeDU+BWA9YdyGNEpCLUL/Y00uIclPj6eRYsW0aNHj2rby8rKGDp0KNdeey233XZbnc5lNpsZO3YswcHBLF++nMjISFJTU/Hyal3rYQghRJNQTlV89Y1x/LziFfj2Lni7Hzywn/TCSjoEV+/aLyq34OfeuNNim5NKpapaI6ep2e0KX+xIJb2wgpOFFWw5lkdGUSV3jGhHbIgXL/54kNxSE2383Wkb6MGlnYNpG+hBu0APYgI9CPU21vjgtysKFRYbj6zYw1W9wp06G2f78XyMOnVVDJlFlRzIKObOEe2cFtPZNChhKS0tZcaMGSxevJjnnnuu2mszZzoy/OTk5Dqf78MPPyQ/P58//vgDnc7RJRYdff6FlkwmEyaTqep5cXHLu5cphBCNQu8B5bmOnwC9bnCMazn0A/z4CErBUMI79q12iNlmR6c597fnnPIcfjnxCxvTNnJPr3voHtS9KVtQb4GeeiosNkoqLXgZm/ZWyj+X/8nXOx0DlGNDvLiyRxiLNx3nvQ1JAEzoHsb8KzoT6Vf32ydDOwTy64MjSMopY2Ssc3syth3Pp08bP/Rax02X9YeyUavgko6uUeH2tAbdEpo7dy4TJkxgzJgxjRLEypUrGTx4MHPnziUkJIS4uDheeOEFbDbbOY9ZsGABPj4+VY+oqKhz7iuEEK3aTacG3SatO7Otz02On4lL+NjyTyK8q38/1ahUlJvP/v/Y3Tm7Gf3VaF7c/iJ/pP/B4j2LmyLqC3K6GmtybnmTv9fpZCX5xQn8/I9LeHRCV24aHE33CB+W3T6I/5vRp17JymntgzwZ2zWkWeqsnIvdrrDjhGPA7WnrD+XQK8q30QvTXah6/ystW7aMnTt3smDBgkYLIikpieXLl2Oz2fjhhx947LHH+M9//sPzzz9/zmPmz59PUVFR1SM1NbXR4hFCiBbFLxp63Qh7vz6zLfYy+GcSuZf9Fz9VKbGWA1UvpRdW8NvBbIZ2CKxxqkprJQ+sfwCVSsW669YxvfN01qWuY82JNc3RkjqLOZWwHM8ra9L3yS6uBGBs15Bq25++Ko7v7x3GoHYte7XoI9mlFJZbGHhq/IrZauf3o7mMig12cmQ11SthSU1NZd68eSxZsgSj0dhoQdjtdoKDg1m0aBF9+/Zl2rRpPProo/z3v/895zEGgwFvb+9qDyGEuGiFdIWik9W3eQSQoWsDQID3mTEsX8SnotNAicfXXL/q+mqDV1ceW0l2eTaPDnyUQLdA/tnvn2jVWt7c+abLDHIF8DbqCPDQk5zbtAnLa2sOA/DsVXFN+j7Osj05H61aRe82fgAknCig1GRlVGfXS1jqNYYlISGB7Oxs+vY9cy/UZrOxceNG3n77bUwmExqNpt5BhIWFodPpqh3bpUsXMjMzMZvN6PWu1S0lhBAux80fLGVgNVUrz1+a41gU0T+8bdU2g96Cuv2/WHbI8fxE8QnCPMOY8/McEnMSGRk5kutirwNAp9Hx8iUv88D6B1iXuo5L21zafG2qRUygR5MmLHa7wqYjuVzVK5xQn8b7ku5Kth/Pp3ukD256x+fv+kPZBHoa6Brmep0A9ephGT16NHv27CExMbHq0a9fP2bMmEFiYmKDkhWAoUOHcvToUex2e9W2w4cPExYWJsmKEELUhfupWxPl+dU2m/JPYkeFh3941bYhsY7vqjqVEQ+dB+8kvsPRgqMk5iQyr888Xh35arVzjIoaBcAf6X80YQPqLybAo0lvCa09kMXJwgpmDT7/JJCWSlEUth/PqzGd2dmDgM+lXgmLl5cXcXFx1R4eHh4EBAQQF+foLsvPzycxMZH9+/cDcOjQIRITE8nMzKw6z6xZs5g/f37V87vuuou8vDzmzZvH4cOHWb16NS+88AJz585tjDYKIUTr537qQ6c8r9rm/IxkchUf0JyZSdMrNJYwzRCsVgOPD3ycH5N/5K61dwFwdcerMWiqL6CoVWu5q+ddfHnoS44WHG3adtSDm16NyWKvfccGUBSF938/Tv8YP/pG+9d+QAuUml9BVrGpavxKemEFh7JKGBnrWrODTmv0ockrV66kd+/eTJgwAYBp06bRu3dv3n333ap9UlJSyMjIqHoeFRXFL7/8Qnx8PD169OC+++5j3rx5PPLII40dnhBCtE5ujjEIVFTvYaksOEmW4ltj9xu7XYuiKaKsNJAr211JgamAIeFD8Dee/cN5RpcZqFQqtmVua+zIG6SgzMwfR/MIa6JbNb8eyGb78XxuG+5atUga07bjeahUVCVk6w/loFGrGN7BNROWCy7Nv379+mrPb775Zm6++eZ6HQMwePBgtm7deqHhCCHExamqh6V6whITGohb9qEau1/XfRivJGr59cRm3rnyOe7qeReRXpHnPL2PwYcRkSP49ui3zOgyo1FDb4hnVu2nsMLC/Cs6N/q5S01Wnl61j+EdA2vMDmpNth/Pp3Ood9WSAOsOOVaK9nF3zhIBtZHFD4UQojUw+IBKU+OWkFtwO6JUOVSaLdW2G7VGtNZo0soOoVFraOPdBrXq/B8JV3e8moP5B9mft79RQ/90SzK3fhxPXqmp9p1PiU/O5+reEXQIbvyK6E98u5f8UjPPXhXnlIUIm0t8cn7V7SCT1cbmo7mMdMHpzKdJwiKEEK2BWu24LfS3W0KG0E4YVBayUmuOPXFTB1Bkya7zWwyLGEaQWxArjqy44HBP+2FPBk9/v58Nh3OY+u4WTtZhBemicgtpBRXENXI5+4yiCh76ajcrdp3kuSlxVbVeWqOs4kqS88qrCsbtSC6g3Gxzyforp8lqzUII0Vq4+9e4JeTXphsAhan7iW7fpdprVnUu3qrQOp9eq9Yyqf0kvjz0JQ/1ewijtuHjRxRF4Z31x3jl50NM7BnO/WM6ctOH27n+vS0svW0QUedZJXhfehEAcRF1m3qrKArpRZWs2p3O1zvTUKtUjO0awqSe4aw5kMWO5AKOZJeQUViJUafh5Wt6MKX3uW+PtQbbjzv+Tvq3dYx9WncwmxBvA13CXHcNP0lYhBCitXAPqJGwBEd2xKToMGXWHMditdvQ17McxZSOU/hg7wesTVnLle2ubFCYJquN+V/vYcWuk9w/piPzRndEpVLx5R2DuWHxVq57bwuf3zaoqvz+3+1NL8JNp6FtoOdZX7fbFb7/M53juWVsPppLcl45OSVnbjeN6RLCW78d5a3fjuKh19An2o8ruocR6efOVb3C8W7itYlcwc6UAsJ8jAR5OmaErT+cw8hOwS59C0wSFiGEaC3c/GvcEtJotZxQh6HOP3aWAxSgfglLtHc0fUP68s2RbxqUsOSXmbnj0x3sTivijWm9uKpXRNVr4b5ufPHXpGXOQDqGVP/GX1huZtWfGXQN90ZzqlaI1WZn/aEcXv75IIezSjHq1FRa7Pi46SiqcJSdf2FKd7qGexPh6wbAd4knSckr57ZL2mHUNayGWEvWu40fH21OZsnWE0T6u3M0u5QHx3ZydljnJQmLEEK0Fu5+kH2gxuY8Yxs8SpJqbPfTRZJrOlHvt7m649U8+vujpJakEuVV94Vnj2aXcMvHOyg3W1l62yD6RvvV2CfE28iy2wdz4/vbmLZoK0vmDKTLqaqrh7NKuO2THRRVWFg8qx8AmUWVzP44ngMZxYT5GJkzrC0GnZqRscH0j/Gn0mI7a0Ly10TpYjSpZzg7TxTwxMp9KAoMbOvv0gNuQRIWIYRoPYy+UFlcY7PNvwMBJ1dityvVKpjGeMWyvWg7FrsFnbrut0HGRo9lwbYFfHv0W+7tfW+djtl0JIe7P9tJuI8bn80ZeN4xKkFeBpbePoiZH2xj+uKtLLl1IOmFFfzji0Si/N1ZcqvjeLPVzg3vb6XCbGPF3UPo06ZmAnQx9p7U1WMTumDQqekW7sPEHmEufTsIZJaQEEK0HgZvMNVMWPw6DSGEfI4c2F1t++GiXSgoVJgr6/U2blo3Lm97Od8d/Q6b3Vbr/ku2nuDmj+LpG+3H8rsGnzdZOc3fQ8/ncwYRHeDBde9t4fZPExjWMZCv7xpSdfwXO1JJzi3j49kDzpqsiPPTatTMv7wLk3qGu3yyApKwCCFE62HwAlNJjc1t+1+OWdGSs2tl1Ta73U6Bso8enpPxNtZ/ZsjVHa8mqzzrvOsLWW12nlq5j8e+3cvMQdG8P6sfXvUY0OrjrmPJrQO4pGMQD43rxH9n9MXDoD0Vv8IHm5K4vHsYsaGuO7NFNB65JSSEEK2F0Rss5WCzVFs7SO/uzV63nninrgeeAECtVmNQQimozDvrqWrTLaAbHf068s3RbxgeObzG68WVFu79fBe/H83l2clxzBzUsAUEvYw63p3Zt8b2DUdySM4r5/XrezXovKLlkR4WIYRoLQynehrO0suiU8z0MCVU2xagb0O2+Wyzh2qnUqmY0mEK61LXkV9ZfWZSSl45V7/zB7tSCvjf7AENTlbOZcPhHJ5ffQBPg5beZ7kVlFlUSUpeOZWW2m9XiZZDeliEEKK1MJwqpGYqPrO20Cm5AX2JTd9Tbdug0KF8c3IzO9KO0i+yQ73f7sp2V/Jawmt8f+x7bup2EwDbkvK4c0kCPm46vpk7lPZBZ6+V0lBFFRZu+nA7AB/c1K/G69/vTucfXyRitSsAeBm1hHgbCfYyVP0M9jYypH1A1ewj0TJIwiKEEK2F8dQH8FlmCqnVGjIJ5K91be8dPIlvvnyTl7d8yJfXvlDvt/Mz+nFp1KV8c+QbZnWdxVcJaTz6zR76Rfvz3xv74Ouub2BDzs3HTcdVvcLZfjy/xjTcpdtT+Pc3e5jcK4Jr+kSSXVJJVrGJ7JJKsktMpBWUszOlgMyiSkxWO7EhXlzVO5xJPcOJ9Kt9ILBwLklYhBCitajqYal5S0ir0aCh+i2SIA8fOrtfzoHS1aQV3U+kT/3rcFzd8WruXHsnD61cyddbtEwfEMUzV8Wh0zTdiINbh7Xlu8R0NhzO5tLOISiKwuJNSbzww0FmDY7mqYndqk3f/juz1c6mIzl8m5jOm78e4eWfDnFp52A+vLl/k8UsLpyMYRFCiNbC7dR4jvKzDKTVu6NXzDU2Pz3yLhTg37+906C37OTTHTV6Vh9fzeNXduWFKd2bNFkB6B7hQ9cwb5ZuT8VuV3hm1X5e+OEg94zqwNOTzp+sAOi1akZ3CeGt6b3545HRAPx2sO6LQArnkIRFCCFaC5vF8dNurfGSyuiNJ+VYrdV7WbqGhBHrdhm7CleRWphbr7fbnXGC8V9Mx2ZXuHfQRG4d1rZZ6nmoVCqmD2zDbwezufnjeD7+I5lnr+rGQ+Nj6/3+/h567ru0A0admuJKSxNFLBqDJCxCCNFaeIaAVxgcWAlH1sDRX+HYOji+EW9zNhqVwrbDqSScKOBgZjEpeeXsSSvipm6zULDz8No36/xWy/ds4cYfb8CqKuaVoYuYO2hiEzaspqt6hWPQqtl7soj3Z/Vj5uCYBp/rhoHRmK12vt+d3ngBikYnY1iEEKK1UKuh+7Xwx5uw75tqL3UCLIqG2z/ZQRluNQ7VBw5nj+p7TpbeQYTn+dfZeXbdUr5Ifhk3ovhs0n/pFNj86/J4G3V8c/dQAj31BJxacbihQn2MDO0QyI97MpkxsHGnYIvGIwmLEEK0JmOehsFzwW4DxV7tkV2h5Tu9P8WVVq5+x1Gh9uVretAlzJtyS28e3v4nCxMW8sqIV856aqvNxs3fPM/usq8I0w3h62tfx9vgvNk1jVnhdkyXEJ5dtZ8Ksw03vaw/5IokYRFCiNZErQav0LO+9Nd+kKQXrkABNFUDVH243zaPxzc/zowuM+gV3KvasfnlJVz91TzyiGeg7wwWTfwXanXrGVXQP8Yfq13hz7RCBrYLcHY44iwkYRFCiIvQ2WbSTGo/ic8PfM7L8S+z5IolqFWOhGRP5gluXn03JnUGszs+yYPDpjZ3uE2uU4gnBq2aPSeLCPDUs+rPDLYm5XE8t4yCMgu+7jom9gznzhHtCfK6sFtQomFUiqIozg6iMRQXF+Pj40NRURHe3lK9UAghGmJH5g5m/zybBcMXcGW7K1mxbytPbnsQFWpeHPo6V8TWrC7bWox/fSMp+eVUWGx4GbQM6xhIx2BP/D30nCysYNn2VKx2hWcnxzG1b6Szw2016vr5LT0sQgghqvQL7ceYNmNYmLCQHSfyWJ68EDeiWDLxv8QGNf/g2uY0fUAUu9OKuKJ7GMM7BmLUVR/Lcs+ojjy7ej8PfbWbpJxS/tmAadSi4aSHRQghRDUnilK48tsJAISqB7P82oX4GKV0PVCtqu5zk+O4sZEXdrwYSQ+LEEKIeqsw23h5VS6V6dcwupsv71x5X6saXHuhVCoVt1/SnuO5Zbz440FGdQ4mwrfmNHHR+OSvUAghBACZRZVc994WfjuYzVsT7+TdSfdLsnIO86/ogqdBy1Mr9zk7lIuG/CUKIYTgz7RCrvq/38ktNfHVnYO5LO7sU6OFg7dRx7wxHVl7IIvs4kpnh3NRkIRFCCEucqv/zOC697YQ6uPGd3OHEhfh4+yQWoQr4sLQqlWs3pPh7FBqlZJXTlYLT6xkDIsQQlykFEXhrd+O8tqaw0zsGc4rU3vUmBkjzs3HXcfQDoH8djCb2UPbOjucs6ow23jxxwN8svUEAOO7hvL2Db3RNvGK2k2h5UUshBDiglVabNy3LJHX1hzmwbGdeHNaL0lWGiA2xIsTeeXODuOcPtx8nKXxqfz78i48e1Ucv+zP5IPfjzs7rAaRHhYhhLjIZBdXctsnOziUVcI7M/pwRfcwZ4fUYrUJcOdkYQUWmx2dC/ZabDiUw8hOQdx2STsAknPL+M+aw4zpGkL7IE8nR1c/rvevK4QQosmkFZRz1f9t5mBmCf+bPUCSlQvUxt8dm10hvbDC2aGc1f6MYjqHnalt8uC4WMJ8jDz+7V4nRtUwF5SwLFiwAJVKxf3331+1bcWKFYwfP57AwEBUKhWJiYn1OueyZctQqVRMnjz5QkITQghxFvllZiw2OyarnZs/iic133VvZ7QE0f4eAKS46L/jkPYB/HYwq+q5m17Dv6/owh/H8ohPzndiZPXX4IQlPj6eRYsW0aNHj2rby8rKGDp0KC+++GK9z3nixAkeeughhg8f3tCwhBBCnEePSF/iHx3DvZd2wGq34+Ouc3ZILdrphRDzSs1OjuTsrukbyd6TxexKKajaNrZLCJ1CPHn7t6NOjKz+GpSwlJaWMmPGDBYvXoyfn1+112bOnMkTTzzBmDFj6nVOm83GjBkzePrpp2nXrl1DwhJCCFEHKpWK/enFDGjrj7dREpYLcXopIQXXXOXm0s7BdI/w4fZPE6p609RqFXNHdWDD4Rz2pBU5OcK6a1DCMnfuXCZMmFDvpOR8nnnmGYKCgrj11lvrtL/JZKK4uLjaQwghRN0czSmla5isu9ba6TRqPry5P+56DXcuSeD08oFX9ginbaAHb6874uQI667es4SWLVvGzp07iY+Pb7QgNm/ezAcffFCv8S4LFizg6aefbrQYhBDiYmKy2NFrZd5FYznXMsIVZhsKCu56x8dtfpmZnScKSC+qoKTSSpCXgRGdggjxNjZZbEFeBp6c2JVbPt7B4axSYkO90KhV3DWyPf9a/ieHs0roFOLVZO/fWOqVsKSmpjJv3jx++eUXjMbG+cctKSnhxhtvZPHixQQGBtb5uPnz5/PAAw9UPS8uLiYqKqpRYhJCiNauQ7AnR7JKnR1Gi1d1S+hvCcvOlAI++P04a/dnYbLa8TZqiQ7wYG96EYoCOo0KT4OWwgoLAMM6BPL4lV2bLHEY1C4AgH3pRcSGOt5jSu8IFq45zPubknh5as8med/GVK+EJSEhgezsbPr27Vu1zWazsXHjRt5++21MJhMaTf0KDx07dozk5GQmTpxYtc1utzuC02o5dOgQ7du3r3GcwWDAYDDU672EEEI4xEX4sDwhFbtdQa1WOTucFkuF49/ur/nKZ9tO8OR3+2gb6MF9ozuSklfOFztS2XPSMV5kfLcQ5gxvR79oPwrLLaw5kMWijUmMe30j/7tlACM6BTV6nNqzLGKp06iZNqAN7244xhMTu+FpcO3SbPWKbvTo0ezZs6fattmzZ9O5c2cefvjheicrAJ07d65xzscee4ySkhLeeOMN6TURQogmMLpLMO9uOMau1EL6RvvVfoA4q5JKRw/J6Q/7hWsPs3DtEW4aHM3I2GBmf1x9+IS3UcufaUVc++4Wgr0MeLvp0KpVFJ/qafk6Ia1JEpbTdWL+Psh6at9IXl97mNV/pnN9/zaN/r6NqV4Ji5eXF3FxcdW2eXh4EBAQULU9Pz+flJQU0tPTATh06BAAoaGhhIY6Vv+cNWsWERERLFiwAKPRWOOcvr6+ADW2CyGEaBx92vgR5mNk2fYUSVguQOapBQVDfYys2JnGwrVHeGhcJzwN2mrJyrs39uX/1h3lcFYJr18fh7tey6YjOZisdqw2O0adhu6RPlzaObhJ4vx5XyZGnZohHQKqbQ/3deOSjkF8EZ/auhKWuli5ciWzZ8+uej5t2jQAnnzySZ566ikAUlJSUJ+le0oIIUTz0KhV3DQkhtd+Ocw/xnYi3Net3udQFIUNh3PoEOxJpJ97E0Tp+rKLTQD8eiCLdzccY2LPcI7llPHNrpOAI1G5LM7xZX1kbBD3Lt3FXZ/tZPGsfvzrss7NFudP+zIZ0SmoavDvX13fP4q7P9vJ0ewSOgS77uBblaKca2xzy1JcXIyPjw9FRUV4e8tUPSGEqE1xpYWxr22gU4gXn9wyAJWq7mNZdqYU8Nyq/exMKeTBsZ24d3THJozUdW1LyuOG97dhsytEB7jj567nUGYJnUI8OZBZwoFnLkPzlzFCZqudu5Yk8PvRXD64qT/DOtZ9sklDncgrY8Qr63ntup5c3Seyxutmq51BC37lmj4RPDqha5PH83d1/fyWbg4hhLhIeRt1vHRNDzYdyeXDzcl1Oqak0sK9S3dx9Tt/sDOlEICp/Wp+CF4sBrYLYN/T4/lx3nACPQ0czS5lyZyBzB7aFrPVTl6Zqdr+eq2ad27sw+D2Acz5JJ4/juU2eYzvbjhGoKfhnOtG6bVqJveKYMXOk5it9iaPp6EkYRFCiIvYyNhg5gxry7Or9rN4YxLn63Q/lFnCVW9vZv3BbF67ridXdA9lQIw/YT71v53Umhh1GuKT80k4UcDC63vRN9qPUbHBGHVqvtqRVmN/g1bDuzf2pX+MP3P+t4Odfymb39gyiipYnpDGnOFtMerOPTHm+v5R5JWZq6075GokYRFCiIvcoxO6cNfI9jz/wwHm/G8H24/nV33TNlvt7EwpYP6KP7nq/35Hr1Wz8t5hXN0nktT8CtoHezg5euertNh4Y+0RrusXyZiuIQD4uOuY2COcz7aewGKr2Wth1GlYNLMf3cK9ufnD7RzIaJpq7Ys2JuGm03DjoOjz7hcb6kXPKF+Wxac2SRyNwbUnXQshhGhyKpWKhy/rTM9IH55bfYDr3tuCVq3Cy+gobKYoEO5j5M4R7bnjkva46R3f1C02OwZt/ctZtDar/8wgr8zM3SM7VNs+e2hbvkpI49tdJ7m2X80SHW56DR/c3J/pi7Yy84PtLL9zMDGBjZcA5paaWLo9hdsvaV+nGivX94visW/3kFFU4ZK9ZpKwCCGEAOCyuDDGdAlhf0Yxu9OKKK6wEOipJybAg34x/tUGjwJoNSpMVpuTonUdX+9MY1A7/xrJRtdwb8Z2DeGd9ceY0jsCrabmTQ1vo47/3TKA697bwoz3t/HJrQNoF+hRrwHQ5/Lu+mNo1WpmD4mp0/4Te4bx7Kr9LN+R5pKDqCVhEUIIUUWrUdMj0pcekb617hvp605aQUXTB+XCCsvNbE3K4/kp3c/6+n2XdmTi27+z6s8MJveOOOs+gZ4Gltw6kGvf3cLo/2zAy6ilS5g3PSJ86B7pQ9cwb0J9jHgatHVOZLKKK/l06wnuHNEePw99nY7xMuq4onsYXyakMndUB5ergCwJixBCiAZpE+DOT3sznR2GU/1xLA+7wjmr03aP9GF052Be/ukgQzsEEuR19iVlwn3d+OG+4WxPzudwVgn70ov4eX8m7/9+vGofg1ZNoKeBIC/DqZ96gjwNRPi50SnEi04hXngYtCScyOfRb/birtdw6/C29WrP9f2j+HpnGluP5zGkfdNPua4PSViEEEI0SNcwbxZtTCKnxHTOD+LWbuOpwnnnK7z33JQ4Jr29mTuXJPD5bQPPOe7Hx13H2K4hjD01cBegoMzMoawSsktM5JaYyCl1/MwtNbH3ZDE5JSaySyqxn5rc5aHXUGa20S3cm6W3D6pRir82/WP8iPRzY/WfGZKwCCGEaB2GdAhApYK1B7KYPsC1y7o3BYvNzvpDOVzePfS8+4X5uLFoZl+uX7SVf6/Yy6vX9qjzrR0/D33VSsvnUmmxcSSrlC1JuXy/O4PZQ2OY3CuiQbd0VCoVV3QPY8XONJ65Kq7GuCVnkmnNQgghGiTYy8jozsF8tPn4eeu3tFaLNiaRU2ri2r61L9Lbu40fr0ztwdc701i8KalR4zi9DtHtl7Tn+1NTzi9k/MnlcaHklpqJT85vxCgvnCQsQgghGuzWYe04nFXKpiNNX7HVlew9WcTCtYe5bXg7uobXbTmYq3pFMHdUexb8eNClC7T1ivIl3MfID3synB1KNZKwCCGEaLBB7fzpFu7d6L0GrqzMZOW+pbuIDfXigbGd6nXsg2NjGdMlhPuWJnI4q6SJIrwwKpWKy+LCXG5AtSQsQgghGkylUnH7Je3YdCSXbUl5zg6nyf1+JJer/m8zmcWVvDmtN3pt/T5G1WoVC6/vRaSfG7f+L578MnMTRXphOod5kV1iwmZ3nVt9krAIIYS4IBN7hNMzypd/f7OHUpPVqbFYbXZySkyk5pdzNLuEvSeLSDiRT2JqIfvTizmaXUp6YQUllZZ6jbtJzS/nzk8TuPGDbfi761l+5xDaBXk2KEYPg5b3b+pHucnG7Z/sIKu4skHnaUr6U0XuXGkxRJklJIQQ4oKo1Sr+c21Pxry2gTfWHubRCV2b7L0URSG7xMShzBKO55aRXlRBemEl6YUVZBRWkFl8ZopvbbRqFX4eesZ0CeaFKd3POnOn0mLjv+uP8e6GY/i663hjWi8m9Qy/4Eq0kX7uLJrVjzs+3cHo/2zgoXGdmDk4xmVm5ZzuOTJb7VVLMTibJCxCCCEuWGG549ZGqalpSvXvSSvihR8OcCCzmMJyC+DoBQj1MRLuayTa353B7QII93UjyMuAm06DQafGqHX8tNoUzDY7ZqudcrOV4korReVmknLL+GhzMsM7BnFF97Cq91MUhZ/2ZvLc6gPklJi4dXhb7hnVAY86rMlTV32j/fj1gZG8/PNBnl61n693nuT5KXF1qjLc1E73sJhsNqB+tVyaiiQsQgghLkilxca/lv9Jryhfnpsc16jnXrEzjdfWHOZkYQWdgr2YM6wtnUK8iA31IsrPvVHKx6fml/Pcqv2MjA3CXa/lcFYJT3+/j81H87i0czBL5gykbSMuSvhXPu46np/SnWv6RvLoN3u56v82M2NgG/4xphMBns4rxne6A8lqc50xLJKwCCGEuCDvb0oiraCC1TP7NvotjeUJaXjotSyY0p3JvSMw6hr/9sQTV3ZjzOsbeOXnQ6hQ8b8tyUT5ufHhzf24tHNI7SdoBH3a+PH9PUP5+I9k3vj1CN/tSufuUR2YPTSmSdpcm8TUQvzcdYR6G5v9vc9FEhYhhBANpigKyxPSmNQrnI4hXo167uziSrYm5fHMVXFMa8JKum0C3LlzRHve/PUI7noND42L5ZZhMecsod9UtBo1c4a34+o+kbz56xH+88shlmw9wT/HxzKpZ3izLka48UguQzsEutQCiJKwCCGEaLBjOaUk55Xz1KRujX7urxLS0GrUTOwZ3ujn/ru7R7bHz13H5XFhhPo4t1fB30PPU5O6MWtwNC/9dJD7v0jkrd+OcF2/KEbEBhEb4nXBg37PZ3dqIXvSCrlhQO0VfJuTJCxCCCEa7M+0IgD6RPs16nlzSky8t+EYU/tG4uPW9IM+jToNs4fWb2XjptYuyJP3ZvYj4UQB729K4vW1h1nw40FCvA1c0jGIEbFBDOsQiK+7vtHeM+FEATd/uJ3ebfyaJVGsD0lYhBBCNNjhrFIi/dzqvSrw+RSVW7jj0x3otRoerGcl2daob7QffaP7UmmxEZ+cz8bDOWw4nMNXCWmoVdDG350IPzfa+HsQHeBOG3/HIzrAHa86XpcKs40v4lN45edDdAv34cPZ/XHXu1aK4FrRCCGEaFFKTZZGTVZOFlYw64Nt5JWZ+Xj2AKfOlHE1Rp2G4R2DGN4xiEcnQEZRBZsO53I0p5TU/HL+TCtk1e50Sv5SvM/fQ0+UvzvRpxKYM7974KbXkF1cyU97M/noj2SKKixM7hXBs5O7uVyyApKwCCGEuED2Rlyp+bOtJ8guNvHdPUMbXEn2YhHm48Z1/auPM1EUhYJyCyfyykjJLyclr5wT+eWk5Jez/Xg+mWepqmvQqrm+fxS3DW9HlL97c4Vfb5KwCCGEaLBwXzdOFqY32vnc9Rr0WrUkKw2kUqnw99Dj76Gnd5ua44oqLTZSTyUwFRYbId5GOgR54ufReONgmookLEIIIRosys+dkkorReUWfNwv/NaQWq3CbHOd9WtaG6NOQ8cQr0afgt4cZPFDIYQQDdbm1C2E43lljXK+fSeLiW2BH6ai6UnCIoQQosFiQ73wMmj57WD2BZ9LURS2Hc9jQFv/RohMtDaSsAghhGgwo07D+LhQViaexF7XZZLP4VBWCbmlZga1C2ik6ERrIgmLEEKIC3J9/yiS88pZf/jCell+PZCNh17DwHbSwyJqkoRFCCHEBekX7UefNr7837pjDe5lsdsVViamc0mnoGZfw0e0DJKwCCGEuCAqlYqHxsU6Ssj/ntSgc3z/ZzqHskq4ZZhrlccXruOCEpYFCxagUqm4//77q7atWLGC8ePHExgYiEqlIjExsdbzLF68mOHDh+Pn54efnx9jxoxh+/btFxKaEEKIZjSkQyB3XNKOl386xIbDOfU6Nq2gnKdW7uOK7qH0j5HbQeLsGpywxMfHs2jRInr06FFte1lZGUOHDuXFF1+s87nWr1/P9OnTWbduHVu2bKFNmzaMGzeOkydPNjQ8IYQQzezBcbGM6BTErR/HszwhrU7HlJmszPnfDjyNWp6b3L2JIxQtmUpR6l9TubS0lD59+vDOO+/w3HPP0atXLxYuXFhtn+TkZNq2bcuuXbvo1atXvc5vs9nw8/Pj7bffZtasWXU6pri4GB8fH4qKivD29q7X+wkhhGgcVpudx7/by9LtqVzfL4rrB0TRO8oXlUpVY99DmSU8+s0eDmQUs+LuocSGSv2Vi1FdP78bVOl27ty5TJgwgTFjxvDcc881OMhzKS8vx2Kx4O9/7q5Bk8mEyWSqel5cXNzocQghhKgfrUbNC1O60z7Ikw9+P84XO1JpF+jB6C7BdA33xsug40BGMX+eLOLXA1lEB3jw4c39JVkRtap3wrJs2TJ27txJfHx8U8QDwCOPPEJERARjxow55z4LFizg6aefbrIYhBBCNIxKpWLO8HbMHtqWLcfyWLEzjR/3ZrJ403EAvI1auoX78OiErswcFI1eK/M/RO3qlbCkpqYyb948fvnlF4xGY5ME9PLLL7N06VLWr19/3veYP38+DzzwQNXz4uJioqKizrm/EEKI5qVRqxjWMZBhHQMBKKqwUGqyEu5jPOstIiHOp14JS0JCAtnZ2fTt27dqm81mY+PGjbz99tuYTCY0mobPn3/11Vd54YUXWLt2bY3BvH9nMBgwGAwNfi8hhBDNy8dNh4/bhS+QKC5O9UpYRo8ezZ49e6ptmz17Np07d+bhhx++oGTllVde4bnnnuPnn3+mX79+DT6PEEIIIVqfeiUsXl5exMXFVdvm4eFBQEBA1fb8/HxSUlJIT08H4NChQwCEhoYSGhoKwKxZs4iIiGDBggWA4zbQ448/zueff05MTAyZmZkAeHp64unpeQHNE0IIIURr0OgjnVauXEnv3r2ZMGECANOmTaN37968++67VfukpKSQkZFR9fydd97BbDYzdepUwsLCqh6vvvpqY4cnhBBCiBaoQXVYXJHUYRFCCCFanrp+fstcMiGEEEK4PElYhBBCCOHyJGERQgghhMuThEUIIYQQLk8SFiGEEEK4PElYhBBCCOHyJGERQgghhMuThEUIIYQQLk8SFiGEEEK4vHqtJeTKThfsLS4udnIkQgghhKir05/btRXebzUJS0lJCQBRUVFOjkQIIYQQ9VVSUoKPj885X281awnZ7XbS09Px8vJCpVI16rmLi4uJiooiNTX1olunSNoubZe2Xzyk7dJ2Z7RdURRKSkoIDw9HrT73SJVW08OiVquJjIxs0vfw9va+6P6QT5O2S9svNtJ2afvFxpltP1/Pymky6FYIIYQQLk8SFiGEEEK4PElY6sBgMPDkk09iMBicHUqzk7ZL2y820nZp+8WmpbS91Qy6FUIIIUTrJT0sQgghhHB5krAIIYQQwuVJwiKEEEIIlycJixBCCCFcniQsQgghhHB5F33Csn79elQq1Vkf8fHxAOTl5XHZZZcRHh6OwWAgKiqKe+65p9aFFkeOHFnjnNOmTWuOZtVJU7bdZDJx7733EhgYiIeHB5MmTSItLa05mlUndWn77t27mT59OlFRUbi5udGlSxfeeOONWs/dGq57Q9veGq47wLx58+jbty8Gg4FevXrV6dyt4bpDw9reWq57SkoKEydOxMPDg8DAQO677z7MZvN5z91arntD2t7s1125yJlMJiUjI6PaY86cOUpMTIxit9sVRVGU/Px85Z133lHi4+OV5ORkZe3atUpsbKwyffr08557xIgRym233Vbt3IWFhc3RrDppyrbfeeedSkREhLJmzRpl586dyqhRo5SePXsqVqu1OZpWq7q0/YMPPlDuvfdeZf369cqxY8eUTz/9VHFzc1Peeuut8567NVz3hra9NVx3RVGUe++9V3n77beVmTNnKj179qzTuVvDdVeUhrW9NVx3q9WqxMXFKaNGjVJ27typrFmzRgkPD1fuueee8567NVz3hra9ua/7RZ+w/J3ZbFaCg4OVZ5555rz7vfHGG0pkZOR59xkxYoQyb968RoyuaTVW2wsLCxWdTqcsW7asatvJkycVtVqt/PTTT40Wb2Oqa9vvvvtuZdSoUefdp7Ve99ra3hqv+5NPPlmvhKU1Xfe6tr21XPcffvhBUavVysmTJ6u2LV26VDEYDEpRUdE5z9UarntD2u6M637R3xL6u5UrV5Kbm8vNN998zn3S09NZsWIFI0aMqPV8n332GYGBgXTr1o2HHnqIkpKSRoy2cTVW2xMSErBYLIwbN65qW3h4OHFxcfzxxx+NGXKjqUvbAYqKivD396/1fK3tukPtbW/N172uWuN1r01rue5btmwhLi6O8PDwqm3jx4/HZDKRkJBw3vO19OvekLY747q3mtWaG8sHH3zA+PHjiYqKqvHa9OnT+e6776ioqGDixIm8//775z3XjBkzaNu2LaGhoezdu5f58+eze/du1qxZ01ThX5DGantmZiZ6vR4/P79q20NCQsjMzGz0uBvD+dp+2pYtW/jyyy9ZvXr1ec/Vmq77aXVpe2u97nXVGq97XbSW656ZmUlISEi1/fz8/NDr9edtR2u47g1pu1Oue5P027iAJ598UgHO+4iPj692TGpqqqJWq5Xly5ef9ZwZGRnKgQMHlG+//Vbp2rWrctddd9Urph07diiAkpCQ0OB21YWz2/7ZZ58per2+xvYxY8Yod9xxx4U1rhZN0XZFUZS9e/cqQUFByrPPPlvvmFrydVeUure9NV73+twS+ruWft3r2vbWct1vu+02Zdy4cTXeQ6fTKUuXLq1zTC3xujek7c647q22h+Wee+6pdaR2TExMtecfffQRAQEBTJo06az7h4aGEhoaSufOnQkICGD48OE8/vjjhIWF1SmmPn36oNPpOHLkCH369KnTMQ3h7LaHhoZiNpspKCioln1nZ2czZMiQ+jeoHpqi7fv37+fSSy/ltttu47HHHqt3TC35uten7a3tul+olnzd66O1XPfQ0FC2bdtWbVtBQQEWi6VG78P5tMTr3pC2O+W6N0ka1ALZ7Xalbdu2yoMPPlin/Tdu3KgAyvHjx+v8Hnv27FEAZcOGDQ2Msmk0dttPD8b64osvqralp6e75CC82tq+d+9eJTg4WPnnP//Z4Pdoqde9vm1vTdf9tAvpYWmp1/20+g66benX/fTA0/T09Kpty5Ytq3XQ7d+1xOvekLY747pLwnLK2rVrFUDZv39/jddWr16tfPjhh8qePXuU48ePK6tXr1a6deumDB06tGqftLQ0JTY2Vtm2bZuiKIpy9OhR5emnn1bi4+OrjuncubPSu3dvl5nqd1pjt11RHNPdIiMjlbVr1yo7d+5ULr30Upea5nja+dp++lbIjBkzqk0JzM7OrtqntV73hrRdUVrHdVcURTly5Iiya9cu5Y477lA6deqk7Nq1S9m1a5diMpkURWm9111R6t92RWkd1/301N7Ro0crO3fuVNauXatERkZWm9rbWq97Q9quKM1/3SVhOWX69OnKkCFDzvrab7/9pgwePFjx8fFRjEaj0rFjR+Xhhx9WCgoKqvY5fvy4Aijr1q1TFEVRUlJSlEsuuUTx9/dX9Hq90r59e+W+++5T8vLymqE19dPYbVcURamoqFDuuecexd/fX3Fzc1OuvPJKJSUlpYlbUn/na/u57hFHR0dX7dNar3tD2q4oreO6K4pjqurZ2n+6V7G1XndFqX/bFaX1XPcTJ04oEyZMUNzc3BR/f3/lnnvuUSorK6teb83Xvb5tV5Tmv+4qRVGUprnZJIQQQgjROKQOixBCCCFcniQsQgghhHB5krAIIYQQwuVJwiKEEEIIlycJixBCCCFcniQsQgghhHB5krAIIYQQwuVJwiKEEEIIlycJixBCCCFcniQsQgghhHB5krAIIYQQwuX9P12kwff1+jKYAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "print(\"here is the final border set\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "173bb175-312b-4d08-a5b5-80a1d5d70416",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(isContiguous(borderUnits,unitNbrs)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6e7e8ccd-8e11-4d6d-970d-b059401e0a3b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the original HD polygon shape assignment csv, e.g. ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv or ./state_HD_output/AZ9HD1tol0.005nW425Mar.csv ./2024state_HD_output/MA2157convexHD2_8nW4_21Mar.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have read back in the original HD shapes\n",
      "County numbers not in input file. Now I will assign each HD center to a county based on the county geoms\n",
      "I found 225 populd HDs whose centers fall outside vtd-based county lines.  Assign to closest county\n",
      "too many to plot individually\n",
      "Ready to capture unit centroids based on these HD poly shapes; see next block\n"
     ]
    }
   ],
   "source": [
    "#OPTION 1 - we already have an ensemble of HDpoly's and their weights\n",
    "#read them in\n",
    "#determine each cluster's required area fraction capture to get 1.00 cluster use across the HD set\n",
    "#then determine total pop (including cluster in/out) for each HD, and total unit use\n",
    "#then move into triage\n",
    "infilename = input(\"enter the original HD polygon shape assignment csv, \"+ \n",
    "                   \"e.g. ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv or ./state_HD_output/AZ9HD1tol0.005nW425Mar.csv\")\n",
    "shapeDF = pd.read_csv(infilename)\n",
    "HDvPop = shapeDF[\"HD-pop\"].to_list()\n",
    "HDweight = shapeDF['HDweight'].to_list() #shapeDF['HDwt'].to_list() or #shapeDF['HDweight'].to_list()\n",
    "HDarea = shapeDF['HDarea'].to_list()\n",
    "#tractPop = shapeDF['tractPop'].to_list()   #we will use vtd-mapped pops instead\n",
    "nHDs = len(shapeDF)\n",
    "hdCPx = shapeDF['centroid x'].to_list()   #note: these may be translated from outcroppings into a convex representation of the map\n",
    "hdCPy = shapeDF['centroid y'].to_list()\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs) ]\n",
    "HDradius0 = shapeDF['HDradius0'].to_list()\n",
    "HDradius1 = shapeDF['HDradius1'].to_list()\n",
    "HDradius2 = shapeDF['HDradius2'].to_list()\n",
    "HDradius3 = shapeDF['HDradius3'].to_list()\n",
    "HDangle0 = shapeDF['HDangle0'].to_list()\n",
    "HDangle1 = shapeDF['HDangle1'].to_list()\n",
    "HDangle2 = shapeDF['HDangle2'].to_list()\n",
    "HDangle3 = shapeDF['HDangle3'].to_list()\n",
    "HDradius, HDangle = list(), list()\n",
    "for t in range(nHDs):\n",
    "    HDradius.append( [HDradius0[t],HDradius1[t],HDradius2[t],HDradius3[t] ] )\n",
    "    HDangle.append(  [HDangle0[t], HDangle1[t], HDangle2[t], HDangle3[t]  ] )\n",
    "print(\"I have read back in the original HD shapes\")\n",
    "popHDlist = list()\n",
    "HDpoly = [dummyPoly for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if HDweight[t] > 0.000001:\n",
    "        popHDlist.append(t)\n",
    "        HDpoly[t] = buildArcPoly(hdCP[t],HDradius[t], HDangle[t], xScale)\n",
    "if \"pigs\" == \"can fly\": #countyNo in shapeDF.columns.values:\n",
    "    HDcountyNo = shapeDF['countyNo'].to_list()\n",
    "else:\n",
    "    print(\"County numbers not in input file. Now I will assign each HD center to a county based on the county geoms\")\n",
    "    HDcountyNo = [-999]*nHDs\n",
    "    for t in popHDlist:\n",
    "        for c, geom in enumerate(countyGeom):\n",
    "            if geom.contains(hdCP[t]):\n",
    "                HDcountyNo[t] = c\n",
    "                break\n",
    "    unassignedList = list()\n",
    "    for t in popHDlist:\n",
    "        if HDcountyNo[t] == -999:\n",
    "            unassignedList.append(t)\n",
    "    if len(unassignedList) > 0:\n",
    "        print(\"I found\",len(unassignedList),\"populd HDs whose centers fall outside vtd-based county lines.  Assign to closest county\")\n",
    "        for t in unassignedList:\n",
    "            minDist = maxD\n",
    "            for c in range(nCounties):\n",
    "                dist = countyGeom[c].distance(hdCP[t])\n",
    "                if dist < minDist:\n",
    "                    minDist = dist\n",
    "                    HDcountyNo[t] = c\n",
    "if len(unassignedList) > 0:\n",
    "    if len(unassignedList) > 20:\n",
    "        print(\"  (too many to plot individually)\")\n",
    "    else:\n",
    "        print(\"FYI, here are those manually assigned HDcenters to their counties\")\n",
    "        for t in unassignedList:\n",
    "            print(t,HDweight[t],\" is the HD row and its weight in the ensemble\")\n",
    "            plotPoly(hdCP[t].buffer(0.1))\n",
    "            plotCenter(int(HDvPop[t]),hdCP[t],6)\n",
    "            plotPoly(countyGeom[HDcountyNo[t]])\n",
    "            plotPoly(MAP,0.2)\n",
    "            plt.show()\n",
    "print(\"Ready to capture unit centroids based on these HD poly shapes; see next block\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2fb6d6e3-6157-4728-a6e5-ea22ede3de03",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OPTIONAL - write the unit topology to a file\n"
     ]
    }
   ],
   "source": [
    "date_ = \"21Mar24\"\n",
    "print(\"OPTIONAL - write the unit topology to a file\")   #New for FL 2/17/24 - include parent VTDs\n",
    "unitParentVTDno = [-999 for u in range(nUnits)]\n",
    "for u in range(nUnits):    \n",
    "    if allUnits[u] %1 == 0:\n",
    "        v = allUnits[u]\n",
    "        unitParentVTDno[u] = parentVTDno[v]\n",
    "\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        #for i,uu in enumerate(surrounders):  #no longer tracked\n",
    "         #   if u == uu:\n",
    "         #       unitVTDlist[u].append(surroundedVTDs[i])\n",
    "                \n",
    "unitNo = [u for u in range(nUnits)]\n",
    "unitCPx, unitCPy = [unitCP[u].x for u in range(nUnits)], [unitCP[u].y for u in range(nUnits)]\n",
    "onBorder = [0 for u in range(nUnits)]\n",
    "for u in borderUnits:\n",
    "    onBorder[u] = 1\n",
    "nbrDF = pd.DataFrame( {\"unitNo\":unitNo,\"centroid x\":unitCPx,\"centroid y\":unitCPy,\"unitPop\":unitPop,\"onBorder\":onBorder,\n",
    "                       \"neighborList\":unitNbrs, \"unitVTDlist\":unitVTDlist, \"unitParentVTDno\": unitParentVTDno} )\n",
    "outname = STATE+\"unitTopologies_\"+date_+\".csv\" \n",
    "outpath = \"state_map_files/\"+outname\n",
    "nbrDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c47fa1ff-2bed-4641-96e9-8dbdb7cb6da9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default use fraction for corner clusters is 1.00, but can enter higher number to account for later discontig rejects\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter maxCCBfrac (e.g. 1.0) prior to discontig shedding 1.06\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on cluster no 0 containing counties [9, 3, 0]\n",
      "halfway there! last use was 0.79853\n",
      "our final fractional capture area to normalize this cluster's usage is 0.35124 from 272 intersecting HDs\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing w/option 1 - set the cluster area thresholds to unitize cluster use\n",
    "#NOTE -- THIS MAY NOT BE RIGHT -- DEPENDS ON HOW WE GENERATED THE HDpoly's\n",
    "print(\"default use fraction for corner clusters is 1.00, but can enter higher number to account for later discontig rejects\")\n",
    "maxCCBfrac = float(input(\"enter maxCCBfrac (e.g. 1.0) prior to discontig shedding\"))\n",
    "CCBuse, CCBareaFrac, CCBarea = [0. for CCB in CCBlist], [0.5 for CCB in CCBlist], [geo.area for geo in CCBgeom]\n",
    "for j,geo in enumerate(CCBgeom):\n",
    "    nIntersections = 0\n",
    "    print(\"working on cluster no\",j,\"containing counties\",CCBlist[j] )\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    HDfrac = [ 0. for t in range(nHDs) ]\n",
    "    for t in range(nHDs):\n",
    "        if HDcountyNo[t] in CCBlist[j]:\n",
    "            HDfrac[t] = 1.\n",
    "        else:\n",
    "            HDfrac[t] = HDpoly[t].intersection(geo).area / CCBarea[j]\n",
    "    idx = np.argsort(HDfrac)\n",
    "    i = nHDs\n",
    "    while CCBuse[j] < maxCCBfrac - 0.5 * nDistricts*np.average(HDweight) and i > 0:  #maxCCBfrac vs. 1.0\n",
    "        i -=1\n",
    "        nIntersections +=1\n",
    "        t = idx[i]\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        if CCBuse[j] > 0.5 and CCBuse[j] - HDweight[t] * nDistricts < 0.5:\n",
    "            print(\"halfway there! last use was\",r5(HDfrac[t]))\n",
    "            plotPoly(HDpoly[t].intersection(MAP.buffer(1)),0.2)\n",
    "        #origHDlist[t].append(unitNo)  #postpone this until main loop\n",
    "        #origHDpop[t] += CCBpop[j]     #postpone until main\n",
    "    if i <= 1:\n",
    "        print(\"WARNING, only\",r5(CCBuse[j]),\" of a district's worth of ensemble intersected the cluster geometry\")\n",
    "        # Add more stuff here to inflate the poly and try again ...\n",
    "    else:\n",
    "        CCBareaFrac[j] = HDfrac[t]\n",
    "        print(\"our final fractional capture area to normalize this cluster's usage is\",r5(CCBareaFrac[j]),\"from\",nIntersections,\"intersecting HDs\" )\n",
    "        plotPoly(MAP,0.2)\n",
    "        plotPoly(geo,0.8)\n",
    "        plotCenter(j,geo)\n",
    "        plotPoly(HDpoly[t].intersection(MAP.buffer(1)),1.3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aad5e7db-93db-43d8-8196-dd6dd02f3014",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am temporarily overriding these area fractions to test our recovery from incorrect starting pts\n"
     ]
    }
   ],
   "source": [
    "#print(\"I am temporarily overriding these area fractions to test our recovery from incorrect starting pts\")\n",
    "#CCBareaFrac = [0.17, 0.25, 0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "217527a2-0acf-43ec-9a25-09e0aecce76d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the main code to determine each HD's pop based on UNIT CP's that intersect the HDpoly\n",
      "We'll later add nearby or jettison faraway contiguous units until we approach the aDP\n",
      "  the HD pops and lists already appropriately include captured corner county clusters\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to enforce areaFrac threshold capture for clusters; helps to even up their usage 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on HDno 0\n",
      "working on HDno 502\n",
      "working on HDno 1004\n",
      "working on HDno 1506\n",
      "working on HDno 2006\n",
      "all done assigning units to HDs based on original HD polygons\n",
      "Here is a scatterplot of active HD pop excess vs relative to target 781460.625\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, compute the current unit usage, plot its histogram weighted by unit pop\n",
      "unit avg and sd usage are 0.94504 0.11204\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to correcting for discontiguity ...\n",
      "CCB cluster 0 with pop 263851.0 originally had use of 1.0575578630585878\n"
     ]
    }
   ],
   "source": [
    "#continuing w/option 1 - now we determine original (nonsmoothed) vtd lists for the original HD polygons\n",
    "print(\"This is the main code to determine each HD's pop based on UNIT CP's that intersect the HDpoly\")\n",
    "print(\"We'll later add nearby or jettison faraway contiguous units until we approach the aDP\")\n",
    "print(\"  the HD pops and lists already appropriately include captured corner county clusters\")\n",
    "#see above block for preliminaries\n",
    "nonUnitCounties = list (set([c for c in range(nCounties)]).difference(set(unitCounties)) )\n",
    "areaFrac = [0.5 for u in range(nUnits)]  #default; we will accept unit counties if we capture half their area\n",
    "alter = input(\"enter 1 to enforce areaFrac threshold capture for clusters; helps to even up their usage\")\n",
    "if alter == 1:\n",
    "    for u in range(nUnits):\n",
    "        if allUnits[u] - int(allUnits[u]) == 0.25:  #a county cluster.  Enforce alternate areaFrac's determined above\n",
    "            CCBno = int(allUnits[u] )\n",
    "            areaFrac[u] = CCBareaFrac[CCBno ]\n",
    "            print(\"enforcing areaFrac capture threshold of\",r5(CCBareaFrac[CCBno]),\"for CCBno, uNo\",CCBno,u)\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] - int(allUnits[u]) == 0.25:  #a county cluster.  Enforce alternate areaFrac's determined above\n",
    "        CCBno = int(allUnits[u] )\n",
    "        areaFrac[u] = CCBareaFrac[CCBno ]\n",
    "        #print(\"enforcing areaFrac capture threshold of\",r5(CCBareaFrac[CCBno]),\"for CCBno, uNo\",CCBno,u)\n",
    "# for c in unitCounties:   #UNCOMMENT TO ENFORCE UNIT COUNTIES TO EQUAL USAGE\n",
    "#    areaFrac[allUnits.index(c+0.5)] = unitCareaFrac[ unitCounties.index(c) ]\n",
    "\n",
    "origHDpop, origHDlist = [0. for t in range(nHDs)], [list() for t in range(nHDs)]\n",
    "for jj,t in enumerate(popHDlist):\n",
    "    if jj%500 == 0:\n",
    "        print(\"working on HDno\",t)\n",
    "    hC = HDcountyNo[t]\n",
    "    if hC in allFusedCounties:  #using a swelling technique as original HDpoly's look skewy\n",
    "        origHDpop[t], origHDlist[t] = clusterSwell(hdCP[t], CCBgeom, CCBpop, allUnits, unitCP, unitPop, unitNbrs, aDP)\n",
    "    else:\n",
    "        if hC in unitCounties:\n",
    "            iCP, iClist = countyPop[hC], [allUnits.index(hC+0.5) ] #automatically include the home county if small\n",
    "        else: #must probe in-county list vs HDpoly intersxn\n",
    "            iCP, iClist =  getPolyPop(HDpoly[t], unitCP, unitPop, countyUnitList[hC])\n",
    "        nonCP, nonClist = getNonHCunits(HDpoly[t], hC, allUnits, unitCP, unitPop, allFusedCounties, areaFrac, countyGeom,\n",
    "                                        neighborCountyList, countyUnitList)  #this will miss county clusters; see below\n",
    "        origHDpop[t]  += iCP + nonCP\n",
    "        origHDlist[t] += iClist + nonClist\n",
    "        for j, geo in enumerate(CCBgeom):  #special for corner clusters -- intersection area must clear threshold estbd in a prev block\n",
    "            unitNo = allUnits.index(j+0.25)\n",
    "            if geo.intersection(HDpoly[t]).area >= areaFrac[unitNo] * CCBarea[j]:\n",
    "                origHDpop[t] += unitPop[unitNo]\n",
    "                origHDlist[t].append(unitNo)\n",
    "print(\"all done assigning units to HDs based on original HD polygons\")\n",
    "HDpopExcess = list()\n",
    "for t in popHDlist:\n",
    "    HDpopExcess.append(origHDpop[t] - aDP)\n",
    "print(\"Here is a scatterplot of active HD pop excess vs relative to target\",aDP)\n",
    "plt.scatter(popHDlist, HDpopExcess)\n",
    "plt.axhline(-0.01*aDP,np.min(popHDlist),np.max(popHDlist),ls=\"--\")\n",
    "plt.axhline( 0.01*aDP,np.min(popHDlist),np.max(popHDlist),ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, compute the current unit usage, plot its histogram weighted by unit pop\")\n",
    "origUnitUse = [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in origHDlist[t]:\n",
    "        origUnitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(origUnitUse,bins=50,weights=unitPop)\n",
    "origUnitUseAvg, origUnitUseSD = getWeightedAvgAndSD(origUnitUse,unitPop)\n",
    "print(\"unit avg and sd usage are\",r5(origUnitUseAvg), r5(origUnitUseSD) )\n",
    "plt.show()\n",
    "print(\"prior to correcting for discontiguity ...\")\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"with pop\",unitPop[u],\"originally had use of\",origUnitUse[u])\n",
    "\n",
    "HDunitList = [origHDlist[t].copy() for t in range(nHDs) ]  #for safekeeping\n",
    "HDvPop = origHDpop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0fdec9f3-be08-4952-9745-bb0e47e5a04d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "599 416761.6348778783\n",
      "1296 242474.0\n",
      "1297 40300.0\n",
      "1299 203941.0\n",
      "1366 60994.0\n",
      "1375 73525.0\n",
      "1398 446938.0\n",
      "1421 444549.8830546292\n",
      "1426 427351.0\n"
     ]
    }
   ],
   "source": [
    "for t in popHDlist:\n",
    "    if HDvPop[t] < 450000:\n",
    "        print(t,HDvPop[t])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70a8d159-68bb-4436-8a07-0ea73d3f5b94",
   "metadata": {},
   "outputs": [],
   "source": [
    "#END OF OPTION 1 -- SKIP AHEAD TO AFTER OPTION 2\n",
    "###########################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "e74fe279-1089-4774-8b1f-2bdc8d30887d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for some states like FL, we move in partial counties before running option 2\n",
      "adding code here to 'push in' state panhandles ...\n",
      "performing squish no 1 for state FL\n",
      "clipPoly 0 intersects [43] counties and a total of 28 units\n",
      "Here is the original cutLine and an alternate based on cut shapes\n"
     ]
    },
    {
     "data": {
      "image/png": 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If3/sLKVShWh6lKsmnQtRTVlxYO0O5s1jSISsJSQatFm9/dDpFX7dL4OqRdOkSLoiblV2QsWih1rNzfdtAiRhEQ2ai40ZYzp48OPOJDRanaHDEUKIhsO3H2Sfgf+6QkG6oaOpc5KwiAbvsUH+pOWVsHhPsqFDEUKIhqPbvTDkn6EUn3aE9S9C0UXDxlSHbqs0vxD1obWrDZO6ePHFX/FM6eaNlZn8byuEEAD0mwPd7oM9/4Pot2H3V9BlZsXP/j3u1MQCBrwAVk71H2ctkB4W0Sg8dUcbCjVaPtx42tChCCFEw2JhDwOfh3GfV5TovxBT8cqIhcyTFa+k7bD3G8hrvD3V8lFVNApeDpbMGRrAvHUnGdvRg84tHQwdkhBCNCxdZl7uXfm3Nc9A7Gpw71i/MdUi6WERjcZ9ffxo72nHM78doVCjNXQ4QgjROOSnwcFICH0A1I33tt94IxfNjrGRmo+ndiIjX8PzS4/etC6QEEI0e4oCa+aAmTX0eNjQ0dwWSVhEo+LvYs17kzuw5lga/9uWYOhwhBCiYfvrLTi1tmJ8i7mdoaO5LZKwiEZnVHsPHh/UmnfWnZSCckIIcS2KAmufg23vwx1zIWi0oSO6bTLoVjRKzwwLIKuojBd+P4qVqTGjO1x/NXAhhGh2Mk9WzAoa+ib0ecLQ0dQKSVhEo6RSqfjvhHYUl2l5fPFBcorbEdbTx9BhCXFLVFKdX9S2S5VvQ8YZNo5aJAmLaLSM1Co+vqsTDpamvLLiOGl5JcwZGoiRWv76CyGauUsVby2dDRtHLZKERTRqarWK18aG4G5nznvrT3IoOZdPp3XGxcbM0KEJIYThlGSDkVnF4ohNhAy6FY2eSqXikQH+RD3Qg9MXChn56d+sOZom056FEM1XYQZYuzap542SsIgmo7e/M2uf7Es3HwciFh3k/p/2czaryNBhCSFE/cuIBQdfQ0dRqyRhEU2Kq405X4d35X/hXYlJzWfwh1t5cdlRzueWGDo0IYSoHzotnN0BPr0NHUmtkjEsokka3tadAQEuRO46y/ytZ/ht/zmGt3VnRs+W9GrlhKoeukm1Oj0bYy7w484kzIzVvDWhPS2dLOv8vEKIZi55F5TmQsBwQ0dSqyRhEU2WuYkRD/Zvxd09WvLb/hSidp9l+rd78HKwYFiIO8PautHVxwETo9rraNTrFY6ez2PtsTRWH0klLa+U7n6OJGUVMeqzv3ltbAiTu3rVS8IkhGimzh8AEyvw6GzoSGqVJCyiybM2M+bePn7M6u3LnsRs/jiayppjqfywIxEzYzXtPe3o5G1PKxdrvB0t8LS3wNrcGEtTY0yMVJSW6yku01KuVTA3UWNqXJHg5JdoyS0p42xWMYkXizh6Lpd9STnklZTjZGXK8HbuTO/eknaedhSUlvPayhM8t/QoKw6f578T2uPn3HRG7wshGhBrVygvAp0G1BaGjqbWSMIimg2VSkXPVk70bOXEG+Pacex8HvvP5nAoOYcNMemczylBf4sTixytTAlyt+HePr70bOVENx8HjK/oubExN+GjqZ0Y16kFr648zvBPtvFAXz8eHuCPnYVJLV2hEEIAJv88etZqwEQSFiEaNbVaRUdvezp62wN+AJRp9aTllXA+t4RijY6Sch1lWj0WpkZYmhphrFZTpqvYBhVJiK25Cd6OFthbmlbrvAMDXdn41AC+/Cue77YnsGhvMo8N9GdGDx+szOSfoxCiFmhLK74aNa0PQ/IXUoh/mBqr8XGywsepbh/VWJga8ezwQGb28uHTP+N4d/0pvtgSz93dWzKzty+e9k3nE5G4ORUKKmRMk6hF2Qlg5dqkisaBTGsWwmBcbc15a2J7tv1nEHf3aMnivcn0f+8vPv8zztChiXomJQ5FrUrZA+7tDB1FrZOERQgD87S34MWRwex6cQgDAlz4efdZqdIrhLg1xdmQ+DcEjTZ0JLVOEhYhGggrM2OmhnqTWaAhPb/U0OEIIRqj47+DSg3B4w0dSa2ThEWIBqSjlz0Ah5NzDRqHEKKRykkC+5Zg7WLoSGqdJCxCNCDudua0crZi6+lMQ4cihGiMii5W1GFpgiRhEaKBGRzkyp8nM9DfalEY0ehI4WNRa4oywcrZ0FHUCUlYhGhgRrRzJ7NAw+6ELEOHIoRobIoywKrpPQ4CSViEaHC6+jjg72LFwr3Jhg5FCNHYFF2UhEUIUT9UKhXTe/iw4Xg653KKDR2OEKKxUJR/HglJwsK8efMIDQ3FxsYGV1dXJkyYwKlTp6rsM2vWLFQqVZVXz549b9p2bm4uEREReHh4YG5uTnBwMGvXrq3Z1QjRREwL9cbWwoQv/4o3dChCiMaiNBf0WklYALZu3UpERAS7d+9m06ZNaLVahg0bRlFRUZX9RowYQVpaWuXrZolHWVkZQ4cOJSkpiaVLl3Lq1Cm+/fZbPD09a35FQjQBVmbGPNy/Fb/tP0d8RqGhwxFCNAaF/8wubKIJS43WElq/fn2V9wsWLMDV1ZUDBw7Qv3//yu1mZma4u7tXu90ffviB7Oxsdu7ciYlJxWJNPj4+NQlNiCbnnt6+LNqbzCsrjrH4wZ6oZCpJkyX/ZUWtKM2r+NrE1hC65LbGsOTlVfxyHB0dq2yPjo7G1dWVgIAAHnzwQTIyMm7YzqpVq+jVqxcRERG4ubnRrl073n77bXQ63XWP0Wg05OfnV3kJ0ZSYmxjx5vh27E7IZvHeFEOHI4Ro6FyDQW1SsZZQE3TLCYuiKMyZM4e+ffvSrt3lRZZGjhzJwoUL2bJlCx9++CH79u1j8ODBaDSa67aVkJDA0qVL0el0rF27lldeeYUPP/yQt95667rHzJs3Dzs7u8qXt7f3rV6KEA1W/wAX7u7uzdzVJziZLkm5EOIGzKzBvT2kHTV0JHVCpdziKmsRERGsWbOG7du34+Xldd390tLS8PHxYcmSJUyaNOma+wQEBFBaWkpiYiJGRkYAfPTRR7z//vukpaVd8xiNRlMlCcrPz8fb25u8vDxsbW1v5ZKEaJBKy3VM+HIHGq2e3x/tjaOVqaFDErWo5DUXjgc/Rei0lw0dimgKfr0HSrLhntWGjqTa8vPzsbOzu+n9+5Z6WGbPns2qVav466+/bpisAHh4eODj40NcXNwN9wkICKhMVgCCg4NJT0+nrKzsmseYmZlha2tb5SVEU2RuYsTXYV3JLynn/p/2UVJ2/UelQohmTm10830aqRolLIqi8Pjjj7Ns2TK2bNmCn5/fTY/JysoiJSUFDw+P6+7Tp08f4uPj0ev1ldtOnz6Nh4cHpqbyaVIIX2crfpgVysm0Au77cR+FGq2hQxK1SJFht6K2aArARAbdEhERQVRUFIsWLcLGxob09HTS09MpKSkBoLCwkGeffZZdu3aRlJREdHQ0Y8eOxdnZmYkTJ1a2M3PmTF588cXK948++ihZWVk8+eSTnD59mjVr1vD2228TERFRS5cpROPX0dueH+8N5dj5PMK+20N20bV7H0UjJDPARG0xMgVtqaGjqBM1Sljmz59PXl4eAwcOxMPDo/L1yy+/AGBkZMSxY8cYP348AQEB3HPPPQQEBLBr1y5sbGwq20lOTq4yNsXb25uNGzeyb98+OnTowBNPPMGTTz7JCy+8UEuXKUTT0KOVE4se7EFydjFjP9/O0XO5hg5JCNGQuAZD5qmb79cI1agOy83G51pYWLBhw4abthMdHX3Vtl69erF79+6ahCNEs9TBy57Vs/vyWNQBJn+9i2eGBvBAv1YYqeVTuhDNnrk9aJrmjEJZS0iIRsjT3oJfHu7FzJ4+vLP+JJPm7+RQco6hwxJCGFpZoRSOE0I0LOYmRrwyJoTfH+2NplzHxK928kjkAU6k5hk6NCGEoeSlgM31J7k0ZpKwCNHIdWnpwJon+vHhlI4cO5/H6M+2M+XrnWyPu2jo0IQQ9UlR4OwuaNHZ0JHUCUlYhGgCjNQq7uzqxdbnBjJ/RhfKdQoP/LyPi4XXrzAtGg4ZfSSuUpQFf74BJ9eCppoLoJ4/CNlnIGhM3cZmIDUadCuEaNiMjdSMbO9BL38n+r37F19sief1cW0NHZYQoqaO/gJ/f1jxvZEpePcA/8HgPwjcO4L6iv6G8lJIPwrbPwZbr4p9miBJWIRoguwtTXlkoD+fbD7NfX38aOlkaeiQhBA1kbgV/PrDmE8gbhMk/AXbPoA/54KFI7QaAK0GVSQnhxbC1ncqjuv/nyZb7VYSFiGaqPv6+PHTziTe3XCSL6d3MXQ4Qojq0mkhaQf0fRKc/CtePR8BbRmc21eRvJz5C/54ChR9RQ+MvQ+MeKcikWmiJGERoomyMDXixVFBPP3LESZ3zWBQoKuhQxJCVEfqISgrAL9/JR/GpuDbp+I1+BUoyYHEbZCwtSJRCRplmHjriQy6FaIJm9DJk35tnHll+XGKy2T9ISEahZOrwdQGWtykZ9TCAULGw5iPKr42cZKwCNGEqVQq/juhHRcLNby1JtbQ4QghbiY3Bfb8D3o8BEbyEORKkrAI0cT5OFnxf2NDWLgnmXXH0m5+gBDCcP58A8xsoe/Tho6kwZGERYhmYHr3loxs585/fj9KclaxocMRV1Bx4zXaRDNy7gAc+7VifIqZzc33b2YkYRGiGVCpVLxzZwccrUy576d95JWUGzokcSWVlI4TwK7PwTkQOocZOpIGSRIWIZoJOwsTfpgVSmaBhscWHqBcpzd0SAKpciuuYGoFppZNto7K7ZKERYhmxN/Fmv+Fd2VvYjbvbzhl6HBEJUlbBOAcABfjK9YEEleRhEWIZqZnKyfu6+PH0gPn0OnlD6MQDYaDb0X9laJMQ0fSIEnCIkQzNKytO9lFZRxOyTF0KEKIS5L3gKVzRX0VcRVJWIRohjp52+NkZcqmmAxDhyKEANCVV8wQ6jAVjEwMHU2DJAmLEM2QkVrF4CBXNsdeMHQoQgiA+M0Vj4I63W3oSBosSViEaKbuCHEjPqOQpItFhg5FCHF4Ebi3r3iJa5KERYhmql8bZ0yN1dLL0gDIHKFmrjgbTq2DjtMNHUmDJgmLEM2UpakxffydJGERwtCOLQUUaD/F0JE0aJKwCNGM3RHixr6kHHKLywwdihDN15FF0GY4WLsYOpIGTRIWIZqxIUFu6PQK0aek7oMQBnEhBlIPyWDbapCERYhmzN3OnA5edqyVVZwNTAr4NVtHFoGFY0UPi7ghSViEaOYmdvZky8kMsgo1hg5FiOZFp4Wjv0KHu8DY1NDRNHiSsAjRzI3v5IlKBcsPnTd0KM2WzBJqps5sgcIL0FEeB1WHJCxCNHOOVqaMbu/B99sTKdPKCs5C1JvDC8G1LXh0NHQkjYIkLEIIHhvUmrS8UpYfOmfoUIRoHkpy4NTaisG2Kuljqw5JWIQQBLjZMLq9Bx9tOk2RRmvocIRo+pJ3g64MAkYaOpJGQxIWIQQAL4wMIre4nM+3xBs6lGZF5gc1U5dK8F84Ztg4GpEaJSzz5s0jNDQUGxsbXF1dmTBhAqdOnaqyz6xZs1CpVFVePXv2rPY5lixZgkqlYsKECTUJTQhxm7wdLXlsYGu++zuBIym5hg5HiKbN1ApMrSHtiKEjaTRqlLBs3bqViIgIdu/ezaZNm9BqtQwbNoyioqqLp40YMYK0tLTK19q1a6vV/tmzZ3n22Wfp169fTcISoknK0+SxMWkjhzIOoSj18zn80YH+hLSw5alfDsujoXokvSzNjK4cfp0JRibQOdzQ0TQaxjXZef369VXeL1iwAFdXVw4cOED//v0rt5uZmeHu7l6jQHQ6HTNmzGDu3Ln8/fff5Obm1uh4IZqSM7lnuHf9veRocgBobd+a8JBwRvmNwtzYvM7Oa2qs5tNpnRn92d888+sRvprRBbVaBgTWPUlZmoXSfMiIge2fwNldMHMFOPkbOqpG47bGsOTl5QHg6OhYZXt0dDSurq4EBATw4IMPkpGRcdO23njjDVxcXLj//vurdW6NRkN+fn6VlxBNxWcHP8POzI5Nkzfx/bDv8bLx4vWdrzNs6TC+OPQFF0su1tm5/Zyt+GRqJzbEpPPu+pN1dh5xBclXmq7sRNg8F5bMgI/bwg/D4fQ6GPsJ+PY1dHSNSo16WK6kKApz5syhb9++tGvXrnL7yJEjmTJlCj4+PiQmJvLqq68yePBgDhw4gJmZ2TXb2rFjB99//z2HDx+u9vnnzZvH3LlzbzV8IRq0xPxE+nr2xd3KHXcrd7p7dOds/lkWxS7i55if+f7494zyG0V4SDhBjkG1fv5hbd15eVQw/10Ti6WpMU8MaY1Kpl7WGclXmrDtH1VUs9WWVryfGlVRd8W+pWHjaoRuOWF5/PHHOXr0KNu3b6+yferUqZXft2vXjm7duuHj48OaNWuYNGnSVe0UFBQQFhbGt99+i7Ozc7XP/+KLLzJnzpzK9/n5+Xh7e9/ClQjR8AQ5BBEVG0WvFr3o71XxuNXH1ocXe7xIROcIlsctZ2HsQladWUU3t26Eh4QzwGsARmqjWovhgX6t0Gj1vL/hFBqtjueGB0rSIkRNlZdWzAga+xkk/Q1BY6Tuyi26pYRl9uzZrFq1im3btuHl5XXDfT08PPDx8SEuLu6aPz9z5gxJSUmMHTu2cpteX1Ft09jYmFOnTuHvf/UzPjMzs+v22AjR2D3Z9UnWJa0j4s8IBngNIDwknO7u3VGpVNia2nJP23uYETyDP5P/JComiif/ehJvG29mBM9gQusJWJlY1UocEYNaY6RW8c66k4xq70E7T7taaVdUJT0sTVh5MZzbV1FzpcfDho6mUavRGBZFUXj88cdZtmwZW7Zswc/P76bHZGVlkZKSgoeHxzV/HhQUxLFjxzh8+HDla9y4cQwaNIjDhw9Lr4loljytPdlx9w4e6vAQqUWpPLDxASavnszyuOVodBWLFBqrjRnuO5zIUZEsGrWIdk7teH/f+9zx2x28v+99zhfWztpA9/TyRa2C4+fzaqU9cQ31NAtM1LOcJDj5R8X3riEGDaUpqFHCEhERQVRUFIsWLcLGxob09HTS09MpKSkBoLCwkGeffZZdu3aRlJREdHQ0Y8eOxdnZmYkTJ1a2M3PmTF588UUAzM3NadeuXZWXvb09NjY2tGvXDlNTWcFSNE+2prbM7jyb38f+zrfDvsXDyoP/2/l/DFs6jPmH51cZeNvepT3vDXiP9Xeu567Au1gRv4JRy0YxJ3rObU+LtjA1oo2rDUclYRGiZtY8W/G17SRZjbkW1OiR0Pz58wEYOHBgle0LFixg1qxZGBkZcezYMX7++Wdyc3Px8PBg0KBB/PLLL9jY2FTun5ycjFotRXaFqA6VSkVPj5709OhJUl4SC2MXsuDEAr499i2jW40mLDiMQMdAANyt3Hm669M83OFhVp9ZTVRsFDPXzaSdUzvCQsIY5jsME7VJjWPo4GUnxeSEqIljSyF+E3SYCpO+MXQ0TYJKqa+KVHUsPz8fOzs78vLysLW1NXQ4QtSpPE0ev8f9zqLYRVwovkAP9x6Eh4TTz6sfatXlDwN6Rc/289uJjIlkd9puXC1duTvobqYETMHOrPrjUSJ3n2XuqhMce304Fqa1N7BXQOlrzhwJnkOPaS8ZOhRRm9a9AHvmw5xYsG1h6GgatOrevyVhEaIRK9eX8+fZP4mMieToxaP42PowI3gG4/3HY2liWWXfuJw4omKj+OPMH6hVasb5j2NGyAxa2bW66XniLhQw9ONtLLg3lEGBrnV1Oc1S6WvOnDVpRb6VDzb9HiGo2xBDhyRqwy9hUFYE4csNHUmDV937tzyXEaIRM1GbMMJvBAtHLyRyZCRBjkG8u/dd7lh6Bx/t/4j0ovTKfds4tGFu77lsnLyR+9vfz5/JfzJ+xXge3fwoO1N33nCcS2tXazztLdh6KrM+LqtZOeowFFDRPvcvcvf9ZuhwRG24EAOxq+FfHxrE7ZEeFiGamLTCNBafXMzS00sp1hYz1GcoYSFhdHTpWGW/Ml0Z65PWExkTycnsk7S2b01YcBijW42+Zvn/l5YfY2f8RaKfG1Rfl9KsnH2jHWkufen56NeGDkXchvLyMkzecgEg+86lOLYfauCIGj7pYRGimfKw9mBOtzlsnrKZ/4T+h5isGMLWhjFj7QzWJ65Hq69Y1NDUyJRx/uP4dcyv/DD8B7xtvJm7ay7Dlg7j80Ofk1lctTelX2tnkrKKOZ9bYojLaiaaxOfHZqkwP4fdC98g661gAH7TD6Lfbzp2ncmqUTt5xeWcSM2jTKuvizAbtVuudCuEaNgsTSyZHjydaUHT2HZuG1ExUTy37TncrdyZHjSdSW0mYWdmh0qlItQ9lFD3UJLzk1l0chFRMVH8cPwHRvqOJDwknGCnYDILNRirVThayvRMIS7JOJ/ImT8+oG3aMroqGg7bD6F46DMM8+9K5Pd7+HjzaXr596pyTJFGS+LFIpKyikjMLCIxq6ji/cUicorLAXhnUnumdZfy/VeSR0JCNCOnsk8RGRPJ2sS1GKuNGe8/nhnBM/C1862yX35ZPsvjlrModhGpRal0detKSWYfygqCWP5YP8ME38QlvdGOdJc+9Hz0f4YORVRDwvE9ZG36kE65mynFlBMtJuE3+hncvC5XZt9wIp2HIw8wMNAFd1tzEv5JSjIKNJX7OFia4OtshZ+TFX7OVrjYmPHCsmN8MrUTEzp7GuLS6p3MEhJCXNfFkov8eupXfjn1CzmlOfT36l+l/P8lWr2WLclbiIqJ4lDmIayN3IjoMouJbSbWWvl/USHpjXakO/em52NSs6OhUvR6jm9fibLzczqUHiAdZ5LazKTtmNnY2Dlevb+i8HDkAXbEX8TPxQo/Z2v8nCwrEpR/Xvb/6rHcdjqTmT/sZfOcAbR2ta6vSzMoSViEEDel0WlYm7CWyNhI4nLiCHAIICw4jFGtRmFmdHmtrjOZhQz9Mor+3WI5kr0Nc2NzJraZyIzgGXhaN49PgXUt6Y32pDv3koSlASrTaDiy/gccj/wPf30i8Ub+5HZ6mI7DZ2FievM17RRFqfbCoa+tPM664+nseWlIs1lstLr3bxnDIkQzZmZkxsQ2E5nQegJ70/cSGRPJ/+38Pz45+AlTA6dyV+BdOFs4sychG1VZS74Y+gCF2ossObmE307/xsLYhQxpOYSw4DA6u3ZuNn9gRfOQn5vFidWf4X/mZ0LJ5qh5KMf7vkHb3mNQ1aBae3X/XeQVl/PbgXPc39dP/i1dgyQsQghUKhU9PHrQw6NHZfn/H0/8yHfHvmOU3yhSk0Np18IJazNjrM3cearrUzzU4SH+SPiDyJhI7ll/D22d2hIWEsZwn+GYGNW8/L8AmSXUMKQnx5G05kPapa+gK2UccRhO8bA5dAgJrdPzfrDxFIoCM3v51ul5Git5JCSEuKY8TR7L4paxKHYR6cXpuJq05dV+j9Dfq/81y/9HxUSxK20Xrhau3B18N5PbTMbe3N5wF9DIJL7RgQvOPej52LeGDqXZij+yg9w/P6JT3haKVRacaDGZNmOewbmFT52fe0f8RWZ8t4e549pyT2/fOj9fQyJjWIQQtSIhM59h33xJUOAhzhbF0tKmJTOCZzCh9YSblv8f6z+WsJCwapX/b+4S3+jABafu9Iz4ztChNDtHo39Hvesz2mkOk6pyJTlgFu3HRGBlY18v5y8t1zHs42142luw8IEeqNXN63GQFI4TQtSK/Wfz0BV2YOHohUSNiiLEKYT39r1XWf4/rTCtct9L5f83TdnE/e3vZ0vylsvl/8/fuPy/aF43qYYgPzeLXT++QIfo+zDTFXGg+0e4vnSCnne/XG/JCsAXW+JJzS3hzQltm12yUhMyhkUIcUN7ErIJdrfFzsKEjhYd6TigY5Xy/z/H/MwdPncQFhxGJ9dOADiaO/JIx0e4r919leX/H978MP52/oSFhDGm1Zhrlv8Xoj7FLHqRXhm/AODwwDLauNd/obaNJ9L54q94nhkaQGtXm3o/f2MiPSxCiBvak5hFj1ZVa0xcWf7/+e7PczL7JOHrwpmxpqL8f7m+olrnv8v/t7RtyRu73rhu+f/mTiWDbuuVWltMgtqXvNmncTZAsnL8fB5PLjnMiLbuRAxqXe/nb2ykh0UIcV3nc0s4l1NCD7+ri2JBRfn/u4PuZmrgVP4+9zeRMZE8t+053CzdmB48nTvb3HlV+f+U/BQWnlxYpfx/WEgYIU4h9Xx1DYukKvVPUUCrNsXOya3ez51ZoOGBn/YT4GbNx1M7yaOgapAeFiHEdf0ZewEjtYrufk433E+tUjPAewDfDf+OpWOX0qtFL7449AVDlw7lv7v/S1JeUuW+3rbevND9BTZP2cxTXZ7iwIUDTP1jKrPWz+LP5D/R6XV1fFUNmIzxqV8G+n1rdXqeWHwIrV7hm5ndsDA1MkgcjY0kLEKI6/pt/zkGB7niaFX9BQ8DHQN5s8+bbJy8kXvb3sums5sYu2IsEX9GsDttd+XAWxtTG+5pew9rJq3ho4EfoVf0PPXXU4xZPoaomCgKywrr6rIaJEUKhdWruMN/E5y7hXzL+n8U9PHm0+xJzOLzuzvjZitjuapLEhYhxDWdyynm2Pk8xnVscUvHO1s482inR9k4eSNv9H6D9KJ0Htz4IHeuvpPlccvR6CoWgDNWGzPUZyg/j/yZxaMX08GlAx/u/5ChS4fy3r73OFdwrjYvSwgSju/BZcU00oy9Cby/fuveZBVq+N/WBJ4Y0oZe/jfuuRRVScIihLimTTEXMDFSMTDQ5bbauVT+f+nYpXw37Ds8rTx5bedrDFs6jC8Pf8nFkouV+7Zzbse7/d9l3Z3rmBY0jVVnVjF6+Wie/utpDl442AymRTf16zO8sycPYr90MheN3HCPWHvNRQvr0i/7U1CppJrtrZBBt0KIa9oed5Hufo7YmNdOmf0ry/+fzT9LVEwUP534ie+Pfc8ov1GEh4QT6BgIgLuVO092eZKHOjzE6jOrm0n5f3kkVB8urnkDE5UFzo+uxc7BuV7PXVBazjfbEpga6l2jx6yigvSwCCGuoigKh1Jy6drSoU7a97H14eWeL7Np8iZmd57NnvQ9TF49mfs33E90SjR6RQ+AhbEFdwXexcoJK/lqyFfYmdnx4t8vMuL3EXx79FtyS3PrJD7RdKl1GjLNfbF3dq/3c0ftTqa4TCdTmG+RJCxCiKucyykhu6iMTi3t6/Q8dmZ23NvuXtZNWsf7A96nVFfK7C2zGbt8LItiF1FcXgxUzELq59WP/w39H8vHLaefVz++PvI1Q5cOZe6uuSTkJtRpnPVBkR6W+qFSwz8JcX3S6RUW7jnL2A4t8LCzqPfzNwWSsAghrnIms2KGTpt6qrxprDZmhO8IFo76V/n/3+7gw/0fVin/39qhNa/3fp1NUzbxQPsHiE6JZvzK8Tyy+RF2nN/RDMa5iJrSako4F3+MlLgj6DVFBinQt+54GudySgjrWf+zkpoKGcMihLhKSk4JRmoVHnb1P+Wyo0tF+f/0onQWnVzE0tNLiYyJZEjLIYSHhFcp//9wx4erlP9/ZPMj+Nv5MyNkBmNbjW185f8N8Mm/qSspzCfj47746M4C4A0cMu1TrzHkFZfz1ppY7gh2pXMdPWZtDiRhEUJcJT2vBHdbc4yNDNcJ627lzpyuc3ikwyOsOrOKqNgowteF08G5A2EhYdzhcwcmahNMjEwY6z+WMa3GcODCASJjInlz15t8dvAzpgRMYVrQNFwtXQ12HaL+pCadwtHVE3NLawD0Oh0n508jUJvOgV5fYGHnDCoVfq071VtMiqLw7NIjFJfpeH1c23o7b1MkCYsQ4irFZTosG0j1TUsTS6YFTeOuwLvYfn47P8f8zH+2/Qc3SzfuDrqbyQGTK8v/d3PvRjf3bqQUpLAodhELYxey4MQCRviOICwkjLZODfeGoSgKPbJWcGr/FgK7DTZ0OI2HopCflc6ZqNl0zt0EwNE+n9Nh6EziDm2lc9EODvT4mK4jwg0S3hdb4tkUc4HvZnbDy8HSIDE0FZKwCCGuUlqua3DlwtUqNf29+tPfqz+nsk8RFRvFl4e/5H9H/8c4/3HMCJ6Bn50fAN423jzf/Xke6/QYy+OWs+jkIv5I+IMurl2YGTKTgd4DMVI3rOsr7fcibH2QgtRTgCQsN1NWWsLxjT/Q5eBL2AKdr/hZhx2z2Zd+Cm1WEgAt2tbvI6BLonaf5cNNp5kzNIA7Qup/vaKmRgbdCiGuQdWgl7W5Vvn/cSvGEfFnBLtSd1Up/z+z7Uz+mPgHHw38CAWFp6KfYvTy0UTGRDao8v9teowydAiNQnmZhhPblpP6XihdDr4EQIliytmwnRQ9cZL9ln0BaB//P0Jyo0kw8sXe+daqNd+O9LxS/rsmhrCeLZk9WKYx1wbpYRFCXMXazIiiMq2hw7ipS+X/72t/H+sS1xEZE8lDmx6ijUMbwoPDGdVqFGZGZpXl/4f6DOX4xeNExkTy0f6P+PLwl0xsPZEZwTPwsvEy6LVcnt0k05v/bf+qr3E9+hVZKkc8ylNoS0V15L9dptH7oc9QlZXiY2UHQLf/rEFRFMwAc5UKOwPF/PGm01iaGvOfEUGoZJ2oWiE9LEKIq1iZGVNQ2vATlkvMjMyY0HoCS8cu5fth31er/P/6O9dzd9DdrE5Yzejlo3nqr4qVow0+LboZ39zycrPY+8s8Dq1bgKKvmDGVk5lG5wMv4Ky9QOfyQzgquRwOfJL9Hd+kx/2fYGRihrlV1bREpVIZNEm4kF/K0oPneGygP7a1VClaSA+LEOIaPOzMuVioQaPVYWbcsMZ63IhKpaK7R3e6e3TnbP5ZFsYurCz/P9JvJOEh4QQ5BgHgZuVWpfx/VGwUs9bPIsQphLDgMEb4jqjX8v9KM5/SnJOVQcb80XTXngbg7N4P8HrpEBaWlhipFGICH8M9dBJG6OnUuqOBo72xxXuTMTNWc1eot6FDaVJq1MMyb948QkNDsbGxwdXVlQkTJnDq1Kkq+8yaNasyu7306tmz5w3b/fbbb+nXrx8ODg44ODhwxx13sHfv3ppfjRCiVvg6WaEocCajyNCh3DIfWx9e6vESmyZv4onOT7AvfR9TVk/hvg338VfyX+j0OuBy+f8V41cw/4752JvZ89L2lxj++3C+OfoNOaU59Rt4E+thKdeUEPv3ctJTznAm5gCx+/5k98I3ODJvMPs/mcrhLb9y8r89cPi8DYHa05wcvphYk7b4KOfY+9sH5ObmUogFTqd/5eypQ3g08GQlv7ScyF1nmdDZU3pXaplKqUH/54gRI5g2bRqhoaFotVpefvlljh07RkxMDFZWVkBFwnLhwgUWLFhQeZypqSmOjtdfEXPGjBn06dOH3r17Y25uznvvvceyZcs4ceIEnp6e1YotPz8fOzs78vLysLW1re4lCSGuobRcR6c3NjJnaAAP9fc3dDi1QqvXsjl5M1ExURzJPIK3jTczgmcwsfVELE2qTjeNz4knKjaKPxL+AGCs/1jCg8NpZd+qzuIrKsjF6kMf9nd7n25jHqqz89QnRa9HN9cJY9X1e48S1L546c6RaNWBXPfe9Jj5FgB73htPj+LoKvuWYIbF6xl1GfJte33VCX7dn8KWZwbiboDCi41Rde/fNUpY/i0zMxNXV1e2bt1K//79gYqEJTc3lxUrVtxqs+h0OhwcHPjiiy+YOXNmtY6RhEWI2jVrwV7KdXoWPnDjHtLG6GjmUaJioth4diOWxpZMajOJ6cHTaWFddTZJdmk2S08vZcnJJWSWZNKnRR/CQ8Lp3aJ3rY+RKMzPwfojX/aHfkC30Q/Watv1JWbPRjRZKXQafg8qI2MKLiRgM78z2YoNqRZtaFd6sHLfg32+psuORyrflz+fgonF5b/dOxe/Q+9T86q0H2scTPAru+v+Qm7RgbPZTP56Fy+NDObB/nWX3DY11b1/39ag27y8PICrek+io6NxdXUlICCABx98kIyMmmXExcXFlJeX37BXRqPRkJ+fX+UlhKg9AwJc2JeYQ3EjmC1UUx1cOvDegPdYf+d6JgdOZln8MkYuG8mc6DkczjhcOfDW0dyRhzo8xIY7N/B237fJLs3mkc2PMGHlBH47/Rul2tJaj03ViGYJnTm+hz2rvyXpjfZcnNuKkHVT6Lx3Dvu2r+fg5iUkfn03AI6qgirJCoB/l8Ec7/cVieqKtXUOfxFW5eehk57kkM8Dle/3u02h1TOb6viKbp1Gq+M/S4/Swcue+/r6GTqcJumWe1gURWH8+PHk5OTw999/V27/5ZdfsLa2xsfHh8TERF599VW0Wi0HDhzAzMysWm1HRESwYcMGjh8/jrn5tbvUXn/9debOnXvVdulhEaJ2nMksZMiHW/lhVjcGBzXtolfF5cWsOrOKhbELScpPor1ze8KCwxjqOxQT9eVxCIqicODCAaJio9iSvAU7M7taK/9fkJeNzcd+HAj9kK6jH7j5AQZwLu4Izh4+mFnaEP3zmwxK+rjyZ4fMe2ChFBGkOc6J8evwXDmFfGNnsnzHUJ56nO5XPN5JNA2kxdNbMLOwRtHrOfjJXRi1n0SnodOvOueRv5ZSenAJvnfNw827TX1c5i35fnsib62JYe2T/Qhyl3tQTdT5I6GIiAjWrFnD9u3b8fK6fv2CtLQ0fHx8WLJkCZMmTbppu++99x7vvPMO0dHRdOjQ4br7aTQaNBpN5fv8/Hy8vb0lYRGiliiKQr/3/mJIkCtzx7czdDj1Qq/oK8v/70nbg6ulK3cH3c2UgCnYmVWdOnup/P+yuGWU6coY7jec8JDwWy7/n5+bhe0nrTjQ/SO6jrq/Ni6nVsVFL6RN9GMAnMMNLy5w0iiQbKfO+I5+Do+W/iQc3ob/ynFkY4sj+RwcspAu/cYAELP+W0J2P0ucUWvavLK/SQ0uzi4qY8iH0QwLcefdyde/b4lrq27CckvTmmfPns2qVavYtm3bDZMVAA8PD3x8fIiLi7tpux988AFvv/02mzdvvmGyAmBmZlbtHhshRM2pVCoGBLiw9XSmoUOpN1eW/z+dc5qomCjmH57PN0e/qVb5/zUJa+ji2oXwkHAGeQ+6xfL/139Sr2jLOLjhJ4qyUuk44RnsbK1v8Upr5ui2lXSIfow8xZIzFu3B1otU/zvoOPgu3LNTyUxNJENbiP/KcQA4kk+sZTeCuwysbCNkxIMU9pqEt7FJk0pWAOatjUWnV3h2eKChQ2nSapSwKIrC7NmzWb58OdHR0fj53fw5XVZWFikpKXh4eNxwv/fff5///ve/bNiwgW7dutUkLCFEHRkQ4MLCPckkZBbSyqV+bo4NRYBDAG/0eYMnuzzJr6d/5ZeTv/DLqV/o59mPsJAwenn0QqVSVZb/nxE8g79S/iIyJpKno5/G09qT6UHTmdRmEtamN//dVaeze++C5+hx/kcAij/6kljL9jjM+AF3T5/bvdzriju+H/8/H6QQCw50fJNBE+8n7expyotzMVGrMP6qA/aqqrHnPnaCYNerP8xa2znVWZyGciq9gKUHzzF3XFtcbORDdF2q0SOhxx57jEWLFrFy5UoCAy9nknZ2dlhYWFBYWMjrr7/OnXfeiYeHB0lJSbz00kskJycTGxuLjY0NADNnzsTT05N58ypGgL/33nu8+uqrLFq0iD59Li9SZW1tjbV19f5IyiwhIWpfSZmOrv/dRMSg1kQMat7roZTpyirL/5/KOUVr+9aEh4Qzym8U5sZVx9qduHiCyNhINiRuwMzYjImtJzI9eDreNtcvJJaXcxG7TyumkBdhwUUTD861GEGXKS9gYV3xOGrvojfofvpDTpi2J6vFIHolfskBj2n0fOSrWr3WlJP7KMg8j1ZTQMmxNfTIW4dGMeGI/RC6562v3G93wLMUaY0YkvBu5bbsx2JwdK1eOYqm4MGf93MqvYDNcwZgaizF429FnYxhud40vgULFjBr1ixKSkqYMGEChw4dIjc3Fw8PDwYNGsSbb76Jt/flf6gDBw7E19eXH3/8EQBfX1/Onj17VbuvvfYar7/+erVik4RFiLoxe/Eh4i4UsP6p/oYOpUFQFIV96fuIjI1ka8pW7M3suSvwLqYFTcPZwrnKvhnFGSw5uYRfT/9KQVkBg7wHERYcRle3rlf9PVX0enYvfB1NcSHlKlOs8+PpWvAXOWoH1A9swsXTj4yUONTf30GKVXs6PPU7hW/5cdpjIqEPf1kr16YpL+fAorn0Tvy8clsR5pgq5ZiodFX2PWkUgG7gy7TtN4G929ZjfGgBdv0ewr/LkFqJpTE4fj6PMZ9v5+OpHZnY2bBrUTVm9VKHpSGRhEWIurEp5gIP/ryfP2b3pZ2noZaSa5gulf9fEb8CrV57Vfn/S0q0JfyR8AdRMVEk5CUQ7BhMeEj4Tcv/J8SdwGrhGDLNfAj+zxZ2bllFmx1zSLMKxqTjXbTb+QR/Bs3F3s4OpVxD+2HhmJlbVWkj7XwS5478RYdBkzn023t0OjOf2M6v0nnCk1X2Sz19EP3i6XgpaVfFoVFMAAUzlZZ449a0enEvaqPGs2RDXVl+6BxP/3KEk2+OwNxEfh+3ShIWIUSt0Or09H/vL/q2cea9yQ27LLqh5Jfls+z0MhadXERaURqh7qGEBYcxwGtAlYG3ekXPztSdRMVEsSN1By4WLkwLmsaUgCk4mDtcs+0ja76h477nKFAssFGVkGQagNm0HylcOJM2uvgq+x6z7E6BbQC2OScosA/CIXQKOX9/R6+8teRgiwOX61Wl3r2ZFoGhAGRmpKH9qg9alRllY78gb8undCncSrxRK7KsAzF3bEHHxO8BGnVhu9o27Ztd7E7IJuHtUajVTWsgcX2ShEUIUWu+2BLH51vi2fXiEBytTA0dToOl1Wv5M/lPImMiq5T/n9B6AlYmVXs+zuSeISo2itVnVgMwptUYwkPC8bevuhRCaVE+5z7sT46pB3YDHyeg+0jKdHpM36oYwHqs75e4dxjMma2L6XniDQAOW/bCuzgWeyWP0yaBBGtPUoYxplwuAnjEYSh+4V9h6+jKib1/0nbtJE4N/YnAPhMoKswnM+U0vsEVEyDyU09j+01FcnPijija9h1bN7/ARkSj1dHh9Y10bmnPkod6GTqcRk0SFiFErckuKqPfu1sI7+XLCyODbn6AuGb5/7uD78bTuuqA1JzSHH47/Vtl+f/eLXoTHhJOnxZ9rjtu8MyhrZVTiHm9ouJ4Yc4FrD8NACDhznVo8y4QsHkWOy0H0aMomnMqd7JN3OhcfriynQIsOd1tLhYH/keIEs+xOxbSvu+YKuc6vmUJ7bY9XPk+89EYXNyaz6Da69mTkMXUb3bLo9JaUC+l+YUQzYOjlSn39Pbl511JZBVqbn6AqFL+f0rgFJbHL2fUslFXlf93MHeoUv4/pzSHRzc/yoSVE/j11K+UaEuuatvU2v6qbdYObuy0GAhAxp6lOPq1I13lQo+iaOKsOuNAPrb6vMr9t/o+AUDX/c8RosRzqPN/adtzRJU2Ew5sImDrYyRQkaActOiNi2vV9Zaaq+3xF7G3NCHEQz4g1xdJWIQQ1fJgv1YYqVR8uOm0oUNpVNyt3Hm669NsmryJl7q/RFxOHOHrwpm+ZjprE9ZSri8HwMTIhLH+Y/llzC/8OOJH/Oz8+O/u/zJ06VA+PfgpF4ouVLbp3fLqFbQVXTktS08BYGXvgrNnG9xeOckRh2EEFR/krFkAiuryeBqznHgSh/yv8r1X9/GojS+X5jq9dyOtVk/GVKXDjWwAAkoOo9Nff+Xl5iKnqIxFe5IZ0dZdxq7UI0lYhBDV4mBlypxhASzem8yRlFxDh9PoWJpYMjVoKisnrOTLIV9ibWrN838/z4jfR/Ddse/I01T0fqhUKrq6deWTQZ+wZtIaxrYay+KTixnx+whe+PsFTlw8AWbWHGkZzvFhSyrbP7xkbuUMH7c+FQsJqoyM6Tx7EftdJ6FS9GRbtWa3yxTOjFpC54fm06pj38rji0uq9uQUbbs8tbkcY/Y7jML8xQSMZHYQb66JoVynZ87QAEOH0qzIGBYhRLVpdXrGfbEDYyMVKx7rI58ub9PpnNMsjF3IH2f+QK1SV5T/D5lBK7tWVfYrLCtkefxyFsYu5Hzhebq4diEsJIzB3oMrZyHtW/EFRkcXY9T/GToOvPm6bZecO7kPTVkZ/h36VNkev3cDFw6vodP0/2JlZdPkyunfqt0JWUz7Zjfv3tmeqaEtDR1OkyCDboUQdSL6VAazFuxj3ZP9CJbn97UiqySrsvx/VmkWfT37Eh4SXln+/xKdXldZ/v9gxsHK8v8T20zExtTGgFfQPJTr9Iz5bDuWZkb8/khvSdhriSQsQog6kVtcRqc3NvHptE6M7ySzRWrTtcr/hwWHMbrV6KvL/2edIComivWJ6y+X/w+ajrft9cv/i9vz/fZE/rsmhtWPy8yg2iQJixCizvR/7y+Ghrjx6pgQQ4fSJCmKwv4L+/k55ufK8v9TAqcwLXAaLpYuVfa9svx/viafQd6DCA8Jv2b5f3HrMgpKGfLBVsZ3bsF/J7Q3dDhNiiQsQog6M3vxIVJzS/j90d6GDqXJS85PZmHsQpbHL6dcX84ov1GEBYcR7BRcZb9bLf8vqufpXw6z9XQmW54ZgL2lFE+sTZKwCCHqTNTus7y26gQHXxmKnaXcDOtDflk+y+MqBt6mFaXRza0bYSFhDPQaWKX8v6Io7EzdSWRMJDtSd+Bs4cy0wGncFXjXdcv/ixu7VCTuvTs7cFeoPHKrbZKwCCHqTFpeCb3mbZFxLAZwqfx/VEwUhzMP17j8f1hwGK0dWhsi9EZJBtrWPUlYhBB1auzn2/G0t+Dr8K6GDqXZOpZ5jMiYSDae3YiFsQWT2kxievD0a5b/X3p6KYtPLq5S/r93i96oVVKO60a+357IW2tiWCUDbeuMJCxCiDr1445E/rsmlp0vDsbVxvzmB4g6k16UzuKTi1l6eimF5YUMaTmE8JBwOrl0qjLwtlxXzoazG4iMiSQmKwY/Oz/CgsMY6z8WC2MLA15Bw3Q2q4hRn/7NpC5evDmhnaHDabIkYRFC1Km84nK6v72Z2YNb8/jgNoYORwDF5cWsPrOaqNgokvKTaOvUlvCQcIb5DsNEfXmskaIoHMw4SFRMFFtStmBjasOUgIpZSG5Wbga8goajXKdn8te7yC0uY80T/bA2M775QeKWSMIihKhzLy47ysYTF/j7+UFYmsof9IZCr+jZfn47kTGR7E7bjauFK3cH383kNpOxN7evsu+5gnMsOrmIZXHL0Gg1DPMdxsyQmbR1bmuY4BuI11edIGr3WZY+2ptO3vaGDqdJk4RFCFHnUrKLGfxhNM8MC+SRAVcvyCcMLy4njqjYqMry/2P9xxIWEnbT8v+dXTsTHhJepfx/c/HTziReW3WCNye0I7ynj6HDafIkYRFC1IuXlx/jj6NpbHlmAE7WZoYOR1xHVkkWv53+jSUnl1wu/x8cTq8WV5f/j06JJjI2kgMXDuBp7cndQXczqc2kZlH+f9WRVJ5acoh7+/hJYcR6IgmLEKJeZBVqGPLRVgYHufLRXZ0MHY64iTJdGeuT1hMZE8nJ7JPVK/+ftB5TtSkT20xkRtCMJln+v7hMy5t/xLB4bwoTOrXgw7s6YSRTmOuFJCxCiHrzy75knv/9GD/d150BAS43P0AY3KXy/5ExkUSnRFer/P9vp38jT5PHIO9BhIWE0c2tW5Mo/787IYuXlx/jfG4Jr49ty9RQ7yZxXY2FJCxCiHqj1yvM+nEfJ87nsfbJfrjZyjTnxiQ5P5lFJxexPG45ZfoyRvqOJDwk/Kry/6Xa0sry/2fyzhDsGExYSBgjfUc2yvL/53NLeHfdSVYdSaWTtz0fTOlAa9em/9iroZGERQhRr7IKNYz67G9aOloS9UAPzIyb10DNpuBS+f9FsYtILUqlq1tXwkPCr1/+PzaSHecvl/+fEjgFR3NHA15B9fwdl8nPu86y5WQGDpYmPD8iiDu7eEkVWwORhEUIUe8OnM1h+re7uSPEjc+ndZYbQCOl1WvZkryFqNgoDmUcwsvaixnBM5jYZuI1y/8vjF3I6jOrUVAY02oMM4Jn0Mah4dbmGfv5do6dz+PNCe2Y2NlTaqwYmCQsQgiD2HAinUejDjC9R0veGNdOkpZG7ljmMSJjI9mUtAlzY/OKgbfBM64q/59bmstvp3+rLP/fy6MX4SHh9PHs0+DK/4/4ZBs9Wznx+rjmXWumoZCERQhhMEv2JvPi8mNM6uzFu3e2x9ioYd2wRM2lF6VXDry9VP4/LDiMzq6dq1X+f0yrMViaWBrwCi7r994WRrdvwQsjgwwdikASFkOHI0Szt/Lweeb8eoQBAS58PLUTdhaNb1CmuFpxeTF/JPxBZExkZfn/sJAwhvsMrzLwVlEUDmUcIjImki0pW7A2sWZKwBTuDrrboOX/9XqFwFfX8X9jQgjv5WuwOMRlkrAIIQwu+lQGTyw+hKOVKV+HdyXIXf5tNhWXyv9HxUSxK21Xjcr/D/UdysyQmbRzrv8FBY+k5DL+yx0sfrAnvfyd6v384mqSsAghGoSzWUU8HHmAhItFPDmkDQ/1b4WJPCJqUq5Z/j84jFb2V5f/XxG/gqjYKM4XnqeTS6eK8v8tB2Osrp+Br08tOcS+pByinxso/x82EJKwCCEajNJyHR9vPs232xIIcrflldHB9G7tbOiwRC3LLs3m11O/Vpb/7+PZh5nBM69d/v9cNJExFeX/W1i1YHrw9Dov/38oOYc75+/k/8aEMKuPX52dR9SMJCxCiAbn6LlcXl15giMpufRr48xrY9vS2tXa0GGJWvbv8v/+dv6EhVQMvP13+f+YrBiiYqJYl7QOU7UpE1pPICw4rNbL/5eW6xj92d9Ymxnz+6O9ZSB4A1Ld+3eN/ovNmzeP0NBQbGxscHV1ZcKECZw6darKPrNmzUKlUlV59ezZ86Zt//7774SEhGBmZkZISAjLly+vSWhCiEagg5c9Kx7rzddhXUnOLubBn/dTUqYzdFiilpkamTLOfxy/jvmVH4b/QEvblryx6w2GLh3KZwc/I7M4E11hEedmP0Gri8a83e9tNty5gbCQMNYmrmX08tE8seUJ9qXvozY+U+v1Ci8tO0ZKdgkfTOkoyUojVaMelhEjRjBt2jRCQ0PRarW8/PLLHDt2jJiYGKysKooJzZo1iwsXLrBgwYLK40xNTXF0vH71w127dtGvXz/efPNNJk6cyPLly/m///s/tm/fTo8ePaoVm/SwCNG4nMksZPRnfzOirTsfT+0ka7c0cSn5KSw8uZDlcctBo+G1v5zxfeEV7BdtwuP111GZVMww+nf5/yDHIMJDwhnhOwJTI9Man7ekTMeLy46y4nAqn93dmXEdW9T2pYnbVC+PhDIzM3F1dWXr1q30798fqEhYcnNzWbFiRbXbmTp1Kvn5+axbt65y24gRI3BwcGDx4sXVakMSFiEan1VHUnli8SGeGNKGOUMDDB2OqAcFZQUcePZh5ne4QIxJBuOKA5hw1pUWRo44zZyJeWAgUDEtelfqLiJjI9l+fjvOFs5MDZzKXYF3Vav8v16vsPpoKu+tP0V2URnvTu4gyUoDVd37920Ny87LywO4qvckOjoaV1dX7O3tGTBgAG+99Raurq7XbWfXrl08/fTTVbYNHz6cTz755LrHaDQaNBpN5fv8/PxbuAIhhCGN69iC1NwS3ll3Eq1Oz7PDAqUybhNndOw0HXuPZ+Fdd/JXyl9ExkRyn+V2Opa6EvZHIX393sLa1BqVSkVvz9709uxNQm4CUbFRfH/se749+i1j/McQFhx2zfL/ucVlPLnkMBfySzmZXsDwtm68ODIYX2era0QjGpNb7mFRFIXx48eTk5PD33//Xbn9l19+wdraGh8fHxITE3n11VfRarUcOHAAMzOza7ZlamrKjz/+yPTp0yu3LVq0iHvvvbdKUnKl119/nblz5161XXpYhGh8vtl2hnnrTjIo0JWP7+qEnaUUmWuKlLIyUl94AbeXXsLY+fIsseMXj7P59484nrKf4yGWTGozielB0/Gy8apyfG5pLkvjlrI4djEZJRn08uhFWEgYfT37olapySgopftbfwIQ4mHL/40NoWcrqbXS0NX5I6GIiAjWrFnD9u3b8fLyuu5+aWlp+Pj4sGTJEiZNmnTNfUxNTfnpp5+4++67K7ctXLiQ+++/n9LS0msec60eFm9vb0lYhGikok9l8OSSw/Rt7cyXM7oYOhxRi/SlpeQsWUJZYhIO06djHnj14z99URFJH8xj9VgXfjv9GwVlBQz2HkxYSBhdXLtULf+vL2dj0kYiYyI5kXUCX1vfirovFgOYMv+AjFVpZOr0kdDs2bNZtWoV27Ztu2GyAuDh4YGPjw9xcXHX3cfd3Z309PQq2zIyMnBzu375ZjMzs+v22Agh6ld2URnJ2cWkZBeTU1xGcZkOTbkeMxM1VqZG2Fma0tLRkpaOljhaXXvg5MBAV14aFcTzvx/j3qRsuvnefJyCaNj0RUVkRy2kPD0Nh6lTcZo167r7qq2ssHX15CHT/jw0+SFWn1lNZEwks9bPIsQphPCQ8Mry/yZqE0a3Gs0ov1EczjxMZEwkb+99GzP1p5i6dMXfvf4r6Iq6V6MeFkVRmD17NsuXLyc6Opo2bW6+fHhWVhaenp588803zJw585r7TJ06lYKCAtauXVu5beTIkdjb28ugWyEaoJTsYv6MvcC+pBwOJueQlne5J9TESIWFiRFmJkZoynUUl+nQ6i//mXG3NaeLjz3dfBwZGuKGt+PlBfF0eoU75+8kv7ScP2b3xdK0fqqfitqlKygge8GP6PLycAibgZlf9Yq0KXo9aS++hMucpzFxc0Ov6NlxfgdRsVHsTN2Jq4Ur04KmMTlgMg7mDlWOPV94nmfWf8nx/E2YGGsZ6juU8OBw2ru0r4tLFLWoTh4JPfbYYyxatIiVK1cS+M9IbgA7OzssLCwoLCzk9ddf584778TDw4OkpCReeuklkpOTiY2NxcamooLhzJkz8fT0ZN68eQDs3LmT/v3789ZbbzF+/HhWrlzJK6+8ItOahbgF6XmlHE7JJeFiIedzSkjPK6WkXEdpuQ61SoWVmTE25sZ4Oljg52RFGzcb2nnaYmZsdMN284rL+e1ACssPnedEaj6mRmo6etvRpaUD7b3s8HWywtvR8pqLHOaVlJOSXczZrGKOns/l0NlcDp/LpUyrJ9jDlomdW3BXN2/sLU2Ju1DAhC930Mvfif+Fd8NIBuE2GtqcHLJ/WIBeU4rTPfdg4ulZ4zb0Gg2pL7yAx5tvYmR9uahgfE48UbFRrD6zGpVKxVj/sYQHh1eW/9fpFfq9u4Xebazp3DaeqJgozhWeM0j5f1EzdZKwXK9OwoIFC5g1axYlJSVMmDCBQ4cOkZubi4eHB4MGDeLNN9/E2/ty1cKBAwfi6+vLjz/+WLlt6dKlvPLKKyQkJODv789bb7113TEv1yIJi2huMgpKiU0r4ExGIYkXi0i4WEjchUIyCirGdtmaG+PpYEkLO3MszYwxM1ajVxSKNFryS7Sk5BRzPrcERQFTYzUdvewYGOjKoEBXCjVaTl8oICWnmJyiMi4WlrHzzEV0eoVhIe6MbO/OgAAXbMxvfXBskUZL9KlM1h5PY9OJC6hUMLGzJxGDWhOfWci9C/bxzqT2TOvesrZ+ZaKOaDMzyfrue1CrcbrvXoxdXG6rPV1eHulz5+Ixbx7qfz36zy7N5rdTv7Hk1BIullykT4s+hIeEU5jTioejDrIiog+dvO0ry/9HxUSx/8L+yvL/E9tMxNZU7hENiZTmF6IJySrUsC8ph5jUPI6n5nP8fF5lYmJmrMbP2Qo/ZytauVjRwcuejl72uNuZ36RV0Gh1nEovYH9SDnsSs/g77iLF/1SeNVaraGFvgaOVKQ6WJnT1cWBqaEtcbGp/7NjFQg2/7EthwY4k8krKmN69JcdT89ErCssf61Pr5xO1o/z8ebJ+WIDa0hKn++/DyN6+9tq+cIGMd9/D4515qE2vHvdUpitjQ9IGImMiic2ORVXuhqdqGMtmzsbCxKLKvtcq/z8jeAYtbSUZbggkYRGikSvT6tlyMoOlB84RfSoDrV7B2dqMdp62tGthRztPW4I9bPF2sKy12iWl5ToOJufgZGWGn7MVpsb1W8K8uEzLjzuTmB99hoJSLQBxb42UVXUbGEVRyPz0U1RGxjjeey9G1nVT46T8wgUy3nsfj3lvXzNpASgt1zLh+0jS2YTe4jh2ZnZMCZjCtKBpuFpWrf+VWZzJklNL+O3Ub+RqchngPYCZITPp5tZNKi0bkCQsQjRSecXlRO05y4IdSVws1NDBy47JXb0YFuJerV6TpiCzQMOjUQfosH4x93d2xrZ/f6z790NlLGMQGoKMDz7AeuBALLt1q/NzlaelkfHhR7R4+y1U/0pa8orLeThqPweTc1nyUE9c7Asry/+X6csY4TuCsJAw2jq1rXJcqbaUNQlriIqNIj43niDHIMKCwxjpN/KWyv+L2yMJixCNjEar46edSXz+ZzwanZ7JXb24p5cvge42hg7NoBSdjqLt2yncug1UKqwHDcKqdy9Uaul1MYTMz7/AoktnrPvU36O68tRUMj76mBbz3q5cc+hEah5PLD5EVlEZ387sRugV0+ALygpYFreMRbGLSC1KpYtrF2aGzGSg90CM1JcHlyuKwq60XUTGVJT/dzJ3YmrQVO4KuAsnCyk4V18kYRGikVAUhY0xF3h7bSznckoI69GSxwe3qZOxIo2dUl5OQXQ0xbt2gZExNkPvwLJrV1RGN57hJGrHxW+/xczfH5vBg+v93GXnzpP20UccDXuKXw+lsj3+IoFuNnw5owv+LtbXPEar11aW/z+UcQhPa09mBM9gYuuJWJtWPSYhN4GFsQtZdWYVekXP6FajCQsJI8BB1riqa5KwCNEI7E/K5v0Np9iTmE3/ABdeHR1MG7fm3aNSXfqyMgo3b6bkyFEURQ+AaUsfrHr3wtTXV3pgaln2z5EYOzthO2pUvZ2ztFxHfEYhO+Ivsi0uk7PH4/HKOY+2Zz+mdPNiQmfPm07Hv+T4xeNExkSyMWkjZsZmTGw9kRnBM65f/v/kYjKKM+jp0ZPwkPDK8v+i9knCIkQDtjcxm/nR8fx1KpMgdxueHxHEwEAXGfh3GxS9nvLkZIp27UKTmAh6BVQqzFr5YdW7NyYtW8rvt4b0paXkr1tPyZHDWHbtht3YMfVy3qjdZ/lhRyJJF4vQK2BuoqZnKyf6t3FhcJDrbS1keKHoQsXA23/K/w/yHkR4SHi1y/+P9R+LpYnlDc4gakoSFiEaoPiMAv67JpboU5n4u1jx1B0BjG7vISsU1xFFr6csMZGiHTspO5cCCqjUKkxbt8aqV29MPFtIEnMN+rIysn9YQPmFdOzHj8e8Y8d6+z1l5JfS772/6NvamTtC3Ahws6ZtCzvMTWr3sV+JtoTVZ1YTFRtFYl4iIU4hhAWHMcJ3BCZGl+sLKYpSWf7/z+Q/sTaxZnLAZO4Ouht3K/dajam5koRFiAbkYqGGz/+MI2pPMi3szXlpZDAj2rnLzdIAFJ0OTXw8RTt3UZ6aCoDKyAizoMCKJMbN9SYtNF2KXk/ur79Scvw4Tvfei5m/f73H8PzSo6w9nsb25wdfs2pybdMrenam7iQyJpKdqTtxsXBhWtA0pgRMuWb5/0Wxi1gWt4wSbQnDfIYRHiLl/2+XJCxCNADFZVq+3ZbIN9vOoFariBjUmnv7+Fb7ubuoH0p5OaUnT1G0axfajAwAVCYmWPXtU6+zYQxFURQKNm6icMsW7KfehWUXw6yWvSP+IjO+28PbE9szvUf9F3W7VP7/j4Q/ABjrP5aw4DD87asmbkXlRayIX1FZ/r+jS0fCQ8IZ0nKIlP+/BZKwCGFgO+Mv8p/fj5KRr2FmLx8iBrXG4TorFYuGRykrI2/tWkoOHsL1P89VWdemKSnev5/c35ZiM/QOrIcMMViv36n0AsK/34OfsxWLH+xp0Mek2aXZLD29lCUnl5BZkkmfFn0ICwmjT4s+VX4/Or2Oree2EhkTyf4L+/Gw8mB60HQmBUyS8v81IAmLEAZSpNHyzrqTRO4+Sw8/R96b3AEfp7qpBCrqnjYnh4wPPsBmyBCDTOetK5r4eLJ+/BGLDh2wnzzZoLOqDpzN4b4f99HC3oKf7+veYKb0l+vKWZ+0vrL8fyu7VoSFhDGm1RgsjKuW/4/NiiUqNoq1iWsxUZswofUEwoLDpPx/NUjCIoQBHE7JZfbig1wsKOOFkUGE9/SRAbVNRO6KFZQcOYLbs8+itmq8Caheo+HCO+9g4tECp1n3XFU9tl5j0Sss2ZfCm3/E0N7Tjm/v6VYv41ZqSlEUDlw4QGRMJH+l/FVZ/n9q4FTcrNyq7Hux5CJLTi7h11O/VpT/9xpAeEg4oe6hMmbtOiRhEaIeKYpC1J5k3lh9grYt7Ph0WifpVWmCtBcvkvHBh9iOHIH1gAGGDueWpL/xBk4PPYSJu2FnuBw4m8Obf8RwOCWXu7p58cb4drU+E6gupBSkVA68LdOVMdxvOOEh4dcs/782cS2RMZHE58YT6BBIWEgYo/xGSfn/f5GERYh6Ulym5eXlx1l+6Dyzevvy0qjgel80UNQfRVHI+/13SmNP4vrMHNSWjacmR96aNQDYjR5tkPOnZBez5WQGa46lsTcxmyB3G96c0K5KWf3GoqCsgOVxy1l0chHnC8/TxbUL4SHhDPIedFX5/91pu4mMieTv839L+f9rkIRFiDqWVahh55ksvtgST3J2Me/c2Z7xnTwNHZaoJ+UXMsj86ENsx41r8DOJlLIysn78CaW8DJeICIPEcCg5h/Dv96LR6ujS0oF7+/gxLMSt0T8y1eq1RKdEExkTycGMg3hae1YMvG0z6ery/3kJLIpdxMr4lVL+/wqSsAhRBy4WavjjSCrLD6dyJCUXgA5ednwwpSMBUlK/2VEUhdxffkVz5kxFb4t5w1pNW1EU8laupHjvPhxn3YN5QP3fGMu0en7cmcgHG0/T3tOOBfeGYmve8Map1IYTF08QGRvJhsQNleX/pwdPx9vGu8p+eZo8lp5eyqKTi8gozqCHRw9mhsxstuX/JWERohZlFJTywYZT/H7wPCpgYKArI9u507eNM262DesmJepfeVoamZ98gt2kO7Hq0d3Q4QBQtGcvecuWYTt2LNZ9DdMDtP54Gv9ZepRCjZaZvXx5YWRQoxincruuLP+fr8mvLP/f1a3rVeX/NyVtIjImkuNZx/G19WVG8AzG+Y9rVuX/JWERohaUluv4YUciX26Jx9RYTcSg1kzq4oWj1FMR/6IoCjmLFlGecg6Xp59CbWaYqbmahASyvv8eyy5dsJs0yWAzU1Kyixn16d+E+jny4sigZrmo57/L/wc7BhMeEn7N8v9HMo/wc8zP/Jn8J1YmVkwOmMz0oOnNovy/JCxC3Kb4jAIeiTpI0sUiwnv58NSQAOwsm2ZXtqg9ZefOk/nppzhMvQvLbt3q7bzanBwufvkVxk6OON5/P2oDTlcu0+qZ8r9dZBdpWPNEvyb7CKi6/l3+39nCmWmB07gr8K5rlv9fHLuY3+N+p0RbwlCfoYSHhNPBpYOBoq97krAIcRv+OJrKf5YexdPegi9ndJHxKaJGFEUh5+efKc/IwOXJJ+s0edBrNGR9+x36gnycH30UI3v7OjtXdeSXlvPQz/vZn5TD0kd708nbsPE0NP8u/z+m1RjCgsNo7dC6yn6Xyv8vjF1ISkEKHV06EhYSxh0t72hy5f8lYRHiFhRptLyxOoZf9qcwrmML5k1qj5VZ0/rjIOpP2dmzZH7+BQ4zpmPZuXOttl2xUOFvlJ44jtP992Pq61ur7d+KjIJS7vtxH2cvFvP+lI6MaNf0H2fcqpzSHH47/Vtl+f/eLXoTHhJ+3fL/UbFR7Evf1yTL/0vCIkQNHUzO4elfDpNZoOG1sSHc1c1bKlOK26bo9WQv+BFdXh4uj0fUSmXZwq1byV+7Dvspk+v1sdONHEyuKK9vYqTm5/u6E+whf4er41rl/2cEz2Cs/9hqlf+fETwDH1sfA0VfOyRhEaKa0vJK+OzPOJbsS6Gjlz2fTO2Er7NUqRW1S5OQyMWvvsLxnnuwaN/ultooPXmS7J9+xqpPH2xHj2owCXVpuY5xX2zHwsSIBfd2l0Hpt0BRFA5mHCQyJpItyVuwNbNlSsAUpgVOa/Ll/yVhEeImsgo1fBV9hsjdZ7EyNWL24DbM7OWDsVHzq4Mg6oei15P13fcopSU4P/YYKuPqPW4sv5DBxa/nY+rljePMcFQmDWcQa7lOz+OLDrL1dCbLHu1DSAv5+3u7LpX/Xx6/HI1WU1H+Pzicts5Vy/9rdBrWJKxp9OX/JWER4jpOpObxy74Ufj9wDpVKxYP9WnFfX19smvlMBlF/Lq2UbGRlBaiA6/8Z1pdqUFtb4fzoYxhZN6yev/zSciIWHmR3QhbzZ3TljhC3mx8kqq2wrJBlccuqlP8PCwljsPfgG5b/dzR3rJyF1BjK/0vCIsS/7EvK5p11JzlwNgcXGzOmdvPm/r5+OEj3tRA1dj63hPsW7CM1r4T/hXelt7+zoUNqsnR6HX+l/HVV+f+JbSZiY1p1BmNiXiILYxey6swqtHptRfn/4DACHQMNFP3NScIixD+SLhbxzrqTrD+RTtsWtjwxpA1Dglzl0Y8Qt+j4+bzKAbY/3hvaLIvCGco1y/8HTcfb9ibl/917EB4STj+vfg2u/L8kLKLZKy3X8dVf8Xy9NQFna1OeGxHI+I6ejX6xNSEMRadXWLAjkQ82niLQzYZv7+mGq40sTWEIGcUZFQNvT/960/L/m89uJjImkmMXj+Fj68OM4BmM9x/fYMr/S8IimrW/TmXw2soTpOWV8MgAfyIGtW4Wa5gIURc0Wh1bT2XyVfQZjpzL5Z5evjw/IggLU/k3ZWgl2hL+SPiDqJgoEvISblr+PzImks3JmyvK/7eZzPRgw5f/l4RFNEs6vcLc1Sf4eddZ+rR24o3x7fB3sb75gUKIKlKyi9mdkMXexGzWn0inoFRLO09b5o5rS1cfR0OHJ/7lUvn/qJgodqTuuGH5/9TCVBafXMzvp3+nWFvMUJ+hhIWE0dGlo0Fil4RFNDul5TqeWnKYjTHpzB3fjrAeLRtlTQIhDG1n/EUejjpAQakWP2crxnbwYGzHFjJWpZE4k3uGqNgoVp9ZDdy4/P/K+JUsjF1IckEyHVw6EB4czh0+9Vv+XxIW0WwoisKFfA1PLDnEkZRcvpjehaEyvVKIGist1/HZn3F8vfUMfVo788X0LthZyHT/xurf5f97efSqKP/v2afKwFudXse2c9uIio1ib/pe3K3cK8r/t5mEnZldncdZJwnLvHnzWLZsGSdPnsTCwoLevXvz7rvvEhh47elSDz/8MN988w0ff/wxTz311A3b/uSTT5g/fz7Jyck4OzszefJk5s2bh7l59QZ0ScLSvJzLKeaPo2lsOJFO/IVCCjRa7C1N+P6ebtJdLUQNaXV6FuxI4n/bEsgtLuPpoQE8MsAfIxmg3iT8u/y/n50fYcFh1yz/fzL7JFExFeX/jdXGjPcfz4zgGfja+dZZfHWSsIwYMYJp06YRGhqKVqvl5Zdf5tixY8TExGBlVbWg0YoVK3j99dfJzMzkueeeu2HCsnDhQu6//35++OEHevfuzenTp5k1axZTp07l448/rlZskrA0fbnFZaw4dJ7VR9M4cDYHcxM1g4Nc6eBlj6+TJV18HGTGghA1lJxVzNO/HuZQcg5TQ1vyUP9W+MnSFE3Stcr/T24zmbuD7r5m+f9fTv3Cr6d+Jac0hxG+I3i99+t1MrOoXh4JZWZm4urqytatW+nfv3/l9vPnz9OjRw82bNjA6NGjeeqpp26YsDz++OPExsby559/Vm575pln2Lt3L3///Xe1YpGEpenSaHX8vPMsn2+Jo7hMx4AAF8Z2bMEdIW5Yy0rKQtySQo2WH7Yn8r+tZ3C0NuXjuzrRzVd6J5uLf5f/H+Y7jPCQcNo5V13nSqPT8MeZP3h337vc2eZOnu/+fK3HUt379239tc/LywPA0fHy/+R6vZ7w8HCee+452rZte71Dq+jbty9RUVHs3buX7t27k5CQwNq1a7nnnnuue4xGo0Gj0VS+z8/Pv8WrEA2VoiisPprGe+tPkpZXyvTuLXliSBtcbMwMHZoQjda+pGxWHj7PmqNpFGl0zOjZkjlDA2RpimbG28ab57s/T0SnCJbHL2dh7ELWrllLZ9fOhIeEM8h7EMZqY8yMzLgz4E5OZp9k+/ntPE/tJyzVdcsJi6IozJkzh759+9Ku3eWM7N1338XY2Jgnnnii2m1NmzaNzMxM+vbti6IoaLVaHn30UV544YXrHjNv3jzmzp17q+GLBm5PQhZvr43lyLk8hoa48eO93WntKtOThbhVOUVlfPpnHD/uTMLLwYI7u3hxX18/Wthb3Pxg0WRZm1oTHhLO9KDpRKdE83PMz8yJnkMLqxZMD64YeGttYk1KQUq9DMC9kVtOWB5//HGOHj3K9u3bK7cdOHCATz/9lIMHD9ZoOml0dDRvvfUWX331FT169CA+Pp4nn3wSDw8PXn311Wse8+KLLzJnzpzK9/n5+Xh7e19zX9F4JGQW8s66k2yMuUAHLzuWPNSTnq0a/uJdQjRUhRot76yLZeWhVBTg5VHBPNDPT6b8iyqM1EYM8RnCEJ8hnMg6QVRMFJ8c+ISvDn+Fq6UrSflJfDLwE4PGeEtjWGbPns2KFSvYtm0bfn5+lds/+eQT5syZg1p9xXQpnQ61Wo23tzdJSUnXbK9fv3707NmT999/v3JbVFQUDz30EIWFhVXaux4Zw9K4ZRVq+PTPOBbtScbN1pz/jAhkbIcWUkZfiNugKArP/HaE9cfTua+PH/f28cXJWh6piurJKM5gedxyMksyGeYzjO4e3evkPHUyhkVRFGbPns3y5cuJjo6ukqwAhIeHc8cdd1TZNnz4cMLDw7n33nuv225xcfFVSYmRkRGKotBEysSI6ygt1/H99kTmR59BpYLnhgdyT29fKaMvRC34eHMcyw6e58MpHbmzq5ehwxGNjKulKw93fNjQYVSqUcISERHBokWLWLlyJTY2NqSnpwNgZ2eHhYUFTk5OODlV7b43MTHB3d29Sq2WmTNn4unpybx58wAYO3YsH330EZ07d658JPTqq68ybtw4jIzkxtVUbYq5wGsrj5NRoCG8lw9PDG6Dg5WpocMSotFTFIXvtyfy2Z9xPDc8UJIV0STUKGGZP38+AAMHDqyyfcGCBcyaNava7SQnJ1fpUXnllVdQqVS88sornD9/HhcXF8aOHctbb71Vk/BEI5FfWs5Ly47xx9E0Bga6sPDBnlL3QYhaUlBazvO/H2XtsXQe7t+Kxwb6GzokIWqFlOYXda5cpyenuIz8knIyC8r4v5XHicso5NNpnRjXsYUM/hOiFmi0OtYfT+fTzXFkFmh4f0oHRrTzMHRYQtxUvdRhEeJKhRotpy8UEJOaT2xaPnEZhZzJKCSrqKzKfv4uVix8oAd9WjsbKFIhGqf9SdksO3SeNq7WtHS0RKtXyCjQEJuWz/rj6WQXldGrlRPfzwqVXkvR5EjCIm6JoigkXCxiZ/xFdp7J4nhqHinZJQAYq1W0drWmjZsNffyd8bA3x9HSFHtLE8yMjQj2sMHY6OYzv4QQFRIvFvHhxlP8cTSNFnbm/LY/hXJdRee4sVqFv4s14zu1YEaPlrR2lRWVRdMkCYuokdMXCvj9wDlWH0klNa8UY7WKTt72jGjrTqC7LUHuNrR2tZZZPkLcIp2+YnZkcbmOA2dziNp1li2nMnCxNuODKR2Z1NkTBbhYqMFYrcLG3ARTY/kAIJo+SVgaIEVRSM0rJe5CAWl5paTllZKeV0JaXikFpVrKdXq0OoVyvR5TIzXWZsbYmBvjYGWKp70FXg4WtHa1IdDdplbW2inSaFl64By/HUjh+Pl8HCxNGNOhBYODXOnu54iVrOcjRK1Iyythyte7OJdTUrktyN2Gdyd1YFynFlU+CLjZykKfonmRO00DcTariL9OZrDjTBZ7E7PJKykHQK0CVxtz3O3Mcbc1p4WdBSbGKozVakyMVJTrFApKtRSUlnM2q5hdZ7JIzy/l0lDq1q7W9PBzpEcrJ3r6OeJagz9yucVlLNiRxE+7kigs1TI4yJXZg9swKNBVPtEJUcuyi8oI/34vZVo9/zcmBHtLEzp42eHvYi0D04VAEhaDKi7T8vvB8yw7eI5DybmYGqnp4mPPfX38aOdpS4CbDR525jUe71FariM+o5CT6QUcOJvDroQsFu5JBqCjlx1jOrRgdAeP664hUqbVE7n7LJ/9GYdGq2NaaEse7N8KT1lzRIg6sTP+Ii8uP0ZhqZbfHulFKxdZN0uIf5NpzQaQW1zGTzvP8uPORPJLtQwIcGFiZ0+GBLtiaVo3OWRGQSm7zmSx9lgaf53KpEyrp7uvI2G9fBjZzh2Tf5Ki/UnZ/Of3oyRdLGJqaMUqrrI6shB1Z/mhczz9yxG6+zny7p0dZHaPaHaqe/+WhKUe6fQKi/Ym8/76k2i0eqaGevNgv1Z4O1rWaxwFpeVsirnA0gPn2HkmCw87c6aGenMhX8OSfcl09rbn7UntCXJvmL9HIZqKvYnZhH23h/GdWvDunR1k7SzRLEnC0sCcTM/nP0uPcvRcHnd18+K54UENoufiZHo+P2xPZMXhVFTAs8MCua+vH0byh1OIOqMoCisPp/LqyuO0a2HHT/d1l3FhotmShKWBUBSFJftSeH3VCXydrHh7Uju6+jgaOqyrZBeVodMrDSKJEqIpS8ku5r9rYthw4gLjOrbgvxPbYWtuYuiwhDAYqXTbAGh1el5ZcZwl+1KY3qMl/zcmpMHWJ3GURQeFqHW7E7I4mJyDprxieYrdCVmcvlCIs7Up82d0YWR7KZ0vRHVJwlJHSsp0PL7oINGnM3lvcgfu6uZt6JCEEPVEURR+2pnE3D9isDEzxtLUGCszI7r5OPL44DYMCnTBRnpVhKgRSVjqgEar46HI/exPyuH7e7oxMNDV0CEJIepJXkk5Ly6rWC35/r5+vDwqWAbTClELJGGpZTq9wtO/HGZPYjY/zgqltyzwJ0SzoNcr/H7wHO9tOEVpuU4e+QhRyyRhqWWfbD7N+uPpfB3WVZIVIZqBuAsFrD2WzppjqZy+UMjYji14cWTQdQszCiFujSQstWjjiXQ+3xLPf0YEMqytu6HDEULUIZ1eYX50PB9sPI21mTGDglx5a2J7Qn0b3ixAIZoCSVhqyZnMQub8eoThbd14dIC/ocMRQtQRRVHYnZDNu+tPcuRcLhGD/HliSBvMjBvmDEAhmgpJWGpBoUbLI5EHcLOtWP5dFioTomk6k1nI80uPsv9sDiEetvz2cC+6SY+KEPVCEpbbpCgKzy89SmpuCSsf7ytTFYVoghRF4bcD55i76gRudub8MKsbgwJd5cOJEPVIEpbbtPTAOdYcS+PL6V1o7SorrArRlKTmlvD7gXP8dSqDg8m5TOnqxWvj2mJtJn86hahv8q/uNqRkFzN3dQx3dvFidAeZvihEY5eSXUz06UyOn8tj/9lszmQWAdDR257I+7vTr42LgSMUovmShOUWKYrCf5Yexc7ChNfGhRg6HCHEbSrSaJk0fyc5RWW0drWml78TDw/wp6WjJT38HOXxjxAGJgnLLVp9NI1dCVn8dF93WbhMiCbg402nySspJ/q5gXg5WBo6HCHEv8h65regUKPlrTUxDG/rxoAA6SIWorH7ZV8y321P5LlhgZKsCNFAScJyC77ZlkBucTmvjpFHQULUl6xCDff8sJeo3Wdrtd2o3Wd5/vdjhPVsyQP9/Gq1bSFE7ZFHQjWUV1zOgu2JhPf0kU9iQtST9LxSZv6wh+TsYraeziQ1t4RHBvrf1uNYnV7h082n+WxLPLN6+/La2BAZpyJEAyYJSw19tz2Bcr2eh6WarRB1rqRMR+TuJOZHn8HcxIg/Zvdj3bE0Pt8Sz087k5jc1Yu7e7Qk0M2m2slGXnE5Kw6fZ9GeZOIyCnh2WAARg1pLsiJEAycJSw0UabT8uDOJ8J4+uNiYGTocIZoEnV4BwEitQq9XuFio4VxuCTvjL/LTrrNkF5UxpasXzw4PxNnajNlD2jA11Juo3WdZuCeZn3adxdxEjae9Bd6Olng5WOBhV7HwYLlOj1anUFymI/FiIXEZhZzLKcFIreKOYFfemthOKtUK0UhIwlIDKw6fp0ij5Z7evoYORYgm4WByDk8uOURqbilqFZTrlMqfWZoaMbKdB08MaY2Pk1WV41xtzZkzLJDHBrVmX1I2py8Uci6nmHM5JRw4m0t6XhoqlQoTIxXGajVmJmr8nKwY3d4Df1drBga44GprXt+XK4S4DZKwVJOiKETuOssdwW4ydkWI25RXXM5XW+P5dlsCIS1suaeXL6bGakyM1LhYm+FuZ04bN+ubLihobmJEvzYuUtBNiGZAEpZqOnIuj5PpBbw0KtjQoQjR6Oj0Cun5pRw7l8fGmHTWHktDr4dnhgXycP9WGBvJhEUhxI3V6K/EvHnzCA0NxcbGBldXVyZMmMCpU6euu//DDz+MSqXik08+uWnbubm5RERE4OHhgbm5OcHBwaxdu7Ym4dWplYfP42JjRp/WzoYORYhGpUyrZ+r/dtHnnS08EnWAIym5RAxszY4XBhMxqLUkK0KIaqlRD8vWrVuJiIggNDQUrVbLyy+/zLBhw4iJicHKquoz5hUrVrBnzx5atGhx03bLysoYOnQorq6uLF26FC8vL1JSUrCxsanZ1dQRvV5hzdE0xnTwwEgtMwmEqC5FUXjjjxMcOZfLp9M60dnbgZZO8khVCFFzNUpY1q9fX+X9ggULcHV15cCBA/Tv379y+/nz53n88cfZsGEDo0ePvmm7P/zwA9nZ2ezcuRMTk4q6Cj4+PjUJrU7FpOWTUaBhaIiboUMRotHILy3n9VUnWHbwPPMmtWd8J09DhySEaMRuqy82Ly8PAEfHy9MC9Xo94eHhPPfcc7Rt27Za7axatYpevXoRERGBm5sb7dq14+2330an0133GI1GQ35+fpVXXdl6OhMrUyO6+cj0RyFu5kJ+KR9tPEXfd7aw4Xg6H0/tyN3dWxo6LCFEI3fLg24VRWHOnDn07duXdu3aVW5/9913MTY25oknnqh2WwkJCWzZsoUZM2awdu1a4uLiiIiIQKvV8n//93/XPGbevHnMnTv3VsOvka2nM+nd2hlTY3nWLgRUDKLdFpfJb/tTsDAxRqWCi4Ua0vNKOXWhAHNjI2b0aMkD/VrhbifTh4UQt++WE5bHH3+co0ePsn379sptBw4c4NNPP+XgwYM1qhqp1+txdXXlm2++wcjIiK5du5Kamsr7779/3YTlxRdfZM6cOZXv8/Pz8fb2vtXLua5ynZ6j53J5dlhgrbctRGOSmlvC1tOZbI+/yJ6ELC4WlhHsYYu5SUUi72RlRhcfB+7v68ewtu7YWcgq5kKI2nNLCcvs2bNZtWoV27Ztw8vLq3L733//TUZGBi1bXu7+1el0PPPMM3zyySckJSVdsz0PDw9MTEwwMrpccyE4OJj09HTKysowNTW96hgzMzPMzOq+2uzpCwWUluvp5G1f5+cSoiEpKdPx16kM9iZmsyP+InEZhahV0MnbnindvBne1p2OXnZS0l4IUS9qlLAoisLs2bNZvnw50dHR+PlVXdk0PDycO+64o8q24cOHEx4ezr333nvddvv06cOiRYvQ6/Wo1RWf1k6fPo2Hh8c1k5X6FHehEIDJX++q9baXPtJLyoKLBkWnV4hNy2dPYjY/70ribFYxLR0t6dnKkafuCKBva2fsLKXnRAhR/2qUsERERLBo0SJWrlyJjY0N6enpANjZ2WFhYYGTkxNOTk5VjjExMcHd3Z3AwMuPVGbOnImnpyfz5s0D4NFHH+Xzzz/nySefZPbs2cTFxfH222/XaBxMXRkc7Mp7kzug1ys33xmozofN7/5OJC6jEE8Hi9uMTojaUVqu45PNcUTtPkuhRoupsZr+bZz5/O7OdPCyN3R4QghRs4Rl/vz5AAwcOLDK9gULFjBr1qxqt5OcnFzZkwLg7e3Nxo0befrpp+nQoQOenp48+eSTPP/88zUJr07YmptwV7faHRvz866zjG7vUblAmxB1oUijJS2vFBdrM4yNVJU1hLR6BZ1OIaOglGPn84g+lcnW05mUlOu4v68fg4Ncae9ph7nJjcviCyFEfarxI6Gauta4lejo6Ku29erVi927d9e4/cYmv7ScE6n5zJIFFEUdWn88jVdXniCzQHPTfdt72nFPLx8mdvHCz9nqpvsLIYQhyFpC9ex0egEA7TztDByJaIjKdXoeW3iQpItFhPo50sHTDo1WT782zrRysb7p8RfyS/m/lcfZcOICdwS7Mau3L/ml5Wj1ClqdHrVKhVqtwkilwtnalNau1jhZ1/3gdSGEuF2SsNSzglItAA6Whh1MLBqeQo2WxxYeZNvpTLr7ObI/KZvFe5OBikeT/zcmhAmdPa9aHkKvV9hx5iJL9qawMSYdOwtTvprRhZHt3GUGjxCiyZCEpZ4p1Pyxmmj68orLufvb3aRkF7PwgR6Vi2wWl2kp0+p5afkxnvntCO+uP0lvfye8HCwxNVYTk5rPgeQcMgs0tHG15oWRwUzu6iU1UIQQTY4kLPXs0kDb1LwSqQAqKp3PLSEmLZ83J7SrsiK4pakxlqbw1YyuHD2Xy5pjaexOyGZfUg4l5ToC3Ky5s4sXQ0Pc6NLSXnpUhBBNliQs9czbsWKl2pTsYrq0dDBwNKKhCGlhS6ivA6sPpxLe89oLf3bwspcpxkKIZksWx6ln1mbGeDtacDgl19ChiAbm7u4t2ZuUTVpeiaFDEUKIBkcSFgPo4efEttOZtzRNXDRdQ4LdMDFSsfHEBUOHIoQQDY4kLAYwvlMLzmQWceBsjqFDEQ2InYUJIR62HDufZ+hQhBCiwZGExQD6+Dvj42TJ99sT6+2cOr1CSnZxvZ1P3Bp/V2vOZBYaOgwhhGhwJGExALVaxezBbVh3PJ1dZ7Lq5ZyvrjxOv/f+YstJedzQkBmrVciTQiGEuJokLAYyqbMn3X0defqXw6Tnldbpub7eeoZFeyoKkD0adZB9Sdl1ej5x6/JKyqWGihBCXIMkLAaiVqv47O7OGKlVTP1mF+dyav9xjVanZ966WN5Zd5InhrTh5JsjaOdpx+urTtT6uUTtyCosk4RFCCGuQRIWA3K3M2fJQz3RKwpjPt/OumNptTZz6PSFAqZ+s5vv/k7kldHBzBkagLmJEff39eNEaj5JF4tq5Tyi9lzIL+Vgcg6hfo6GDkUIIRocSVgMzNvRktWP9yXU15FHFx5kyte7WHHoPPml5TVqR1EU4jMK2HUmizm/Hmb4J9vILNDw68M9eaBfq8r9BgW6Ymqs5ou/4mv7UsRt0Or0fLI5DlNjNeM6tjB0OEII0eBIpdsGwN7SlG/Cu7It7iJfbInjqV8Oo1ZVrOjcpaUDnvYWmBipyCku51R6AUVlWmwtTDBSqVCpKh4jxGUUcCFfA4CztRlzx7VlWmhLTI2r5qQWpkY4Wpqy9MA5Xh/XFmuzxvu/QLlOz087k3CxMaNtCzuSLhaRcLGQe/v4YWLUuHLxt9bGsnhvMs8ND5RHQkIIcQ0qpYlUL8vPz8fOzo68vDxsbW0NHc5tSckuZueZi+xOyObY+TzSckso1yvYmhsT4GaDjbkxhRoten3FYoqOVqa0dLSit78TLezN8XKwxNzE6LrtJ2QWMvjDrYzt2IJPp3ZCrW6c68/sSchi6je7r9q+/5U7cLY2M0BEtyb6VAb3/riP2YNaM2dYoKHDEUKIelXd+3fj/XjdhHk7WjLVsSVTQ1vWSfutXKz5/O7OzF58CK1Oz4d3dcTStPH9r9DdzxEvBwvO5ZSw6IEefPpnHPml2kaVrCReLOKJxYcYFOjKk3cEGDocIYRosBpXv7moNWM7tuB/4V3ZejqTO+fv4ngjrK6qUqn4X3hXAEq1OvSKQqCbtYGjqr4d8ReZ8vVOnG3M+GRaJ4waaU+XEELUB0lYmrHhbd35/dHeaMp1jPl8OzN/2MvivckcPZdLam4JucVlxGcUotHqDB3qdYV42BLoZsPyQ6lkFZbhYtPwe1eyCjW8t/4k4d/vIcjdll8f7oWtuYxbEUKIG2l8zwFErQr2sGXj0/1ZcyyNH7Yn8vLyY+j/NarJ1FhNRy87uvo40s3Hge6tHBvMDValUtHL34kd8RcxMVJTptUbOqQqsovKAMgvKSc2LZ/NsRmsPpKKSgVPDGnD7MFtpGdFCCGqQRIWgbGRmvGdPBnfyZOSMh1xGQXkFJdTUFqOlZkxCZlFHDibzbKD5/h66xk6t7Rn+WN9DB12JUcrU3KKy+nS0p7jqfmGDqfS3NUnWLAjqco2b0cL5gwLYGo3bxysTA0TmBBCNEKSsIgqLEyN6OBlX2XboEC4v68fOUVldH5zE3cEuxkmuOtwsDIlp7iMYW3defa3I6TnleJuZ26wePR6hfc3nmLBjiRmD25NO087LEyMCPKwwdXGcHEJIURjJgmLqLZ1x9NRq+Cubt6GDqUKW3NjdHqF3v5OWJgYsXDPWZ4x0PTg4+fz+O+aGPYkZvPK6GDu7+uHSiWPfIQQ4nZJwiKqbVNMOr38nRrcwNZDybl42JnjYWfOjB4t+XFHEg/0a1VvBdgu5Jdy8GwO60+ks/JwKv4uVvx8X3f6tXGpl/MLIURzIAmLqLa0vFJ6NLB1bhRFYXPsBe4IdkOlUvFQ/1ZE7j7L938n1FkRttJyHV/9Fc+qI6kUlenILKioMOztaMFbE9sxtZs3xo2s0q4QQjR0krCIartYqMGpgRVlO3WhgHM5JdwRUjGuxtXWnPv7+vH5X/H4u1ozvpNnrZznTGYhu85kcTA5h80xFygu03FXqDfO1mYEu9vQxccBN1sZnyKEEHVFEhZRbUZqFeW6hjVteHPMBaxMjejZ6nLPz7PDAknPK2XOr0fIKyknvKdPjcaR6PQKJ9PzScgs4kxmIfuTctgefxEjtYpgDxtm9PRhajdvfJ2t6uKShBBCXIMkLKLa/JytSMgsMnQYlc5kFvLt34kMb+uOmfHltZPUahXvTe6AnaUJ/7fyBH8cTeO54YF083G4YeISd6GA1UfTWHn4PGezioGKKdPtPe14f3IHxnRogYXp9ddoEkIIUXckYRHV1srFmgNJOYYOA6ioFnvvgn242pjx2ri2V/3c2EjNa2Pb0j/AhXfXnWTK17vwdbJkaIgbLjZm2JibYGlqROLFIo6fzyMuo5CzWcXYmBkztK0b8ya1J8TDFntLqZUihBANgSQsotp6tnJi0Z5kYtPyCfYw3IrYWYUa7vtpP8VlOhY+0OOGs4EGBbrSv40LO+Iv8sfRVNYeSye/pJzCMi2KAvaWJrT3tGNwkCt9/J3pF+BcpbdGCCFEwyAJi6i2ke3c8bAz5/vtiXwwpaNBYjiRmsdDPx+gtFzHj/d2x9vR8qbHGKlV9A9woX/A5WnGer1CcbkOK1MjqZMihBCNgMy9FNVmYqTmgX6tWHbwHHsSsur13AWl5Xy99QyT5+/CwcqEVbP70t7L7pbbU6tVWJsZS7IihBCNRI0Slnnz5hEaGoqNjQ2urq5MmDCBU6dOXXf/hx9+GJVKxSeffFLtcyxZsgSVSsWECRNqEpqoJ7N6+xLq68gTSw6RkFlYp+dSFIXj5/N4a00Mfd7ZwocbT3FnV09+e7g3nvYWdXpuIYQQDUuNHglt3bqViIgIQkND0Wq1vPzyywwbNoyYmBisrKpO8VyxYgV79uyhRYsW1W7/7NmzPPvss/Tr168mYYl6ZKRW8fndnZn+3R4mf72L7+/pRueWDtU+PiW7GAcrU6zNqv6vp9Hq0OoU0vNLibtQyKHkHDbFXCDhYhGOVqbc1c2bB/q1MugaQUIIIQynRgnL+vXrq7xfsGABrq6uHDhwgP79+1duP3/+PI8//jgbNmxg9OjR1Wpbp/v/9u4+pql7jQP4t9VSOsaLbZGKcB2XzZdrE1+4L74sG2NM/wAl2R8Tg+zNS2ZYQcNcMjIzZLnGbMlNRrJsl+wui7tzI8ZFl6gxg+iusKnTQnVg4lzGxJcSvQ5oRSkUnvuHWb29CD3FHnso30/SP/idnp/nPI+Pffh5es4wSkpKUFtbi+bmZvT29oZzaPQAzUyKx95Ny7Fx12msqz+BDcvmwJH3KMzjPH34Pzd9+NuBc9jvugq9DrAlxcOWHA9zghH9Pj9cl3pxe2g48P5ZyfFYkW1FzdqFWJlt4Z1jiYimuPu66Lavrw8AYDbfvWnXyMgISktL8frrr2PhwtFfNx3L22+/jdTUVGzcuBHNzc0h3+/z+eDz+QI/ezyeMI6c7lfKQ3HY/de/4J/NP+Mf//4Ze05fwspHLfjjHDMyzSY8FDcdcdP16LpxCyc6b+DAWTdMhmnYvuYPMMVNw6Vfb6PbM4Bf+wdheTgO5bnZyDCbYEsyIcuawJUUIiIKMuGGRURQVVWFxx9/HHa7PTD+zjvvYPr06aisrFQ817fffouPP/4YLpdL8T47d+5EbW1tOIdMERZvmAZH3mNY/+ff4V8nLuLEzzfw98bzGBi6ezdcnQ74vTUBW/IfQ/GffjfuKgwREdFYJtywOBwOnD17Fi0tLYExp9OJuro6tLa2Kv72hdfrxYYNG/DRRx/BarUq/vOrq6tRVVUV+Nnj8SAzM1P5CVDEWB42Ykv+XACAf3gE3gE/+gf9GBgawewUE+8OS0RE900nIhLuThUVFdi/fz+OHTuGrKyswPh7772Hqqoq6PV3rzcYHh6GXq9HZmYmfvnll1FzuVwuLFmyBNOm3f1QGxm58xu6Xq/H+fPnkZ2dHfKYPB4PkpOT0dfXh6Sk6N3UjIiIiJRT+vkd1gqLiKCiogL79u3DN998E9SsAEBpaSny8/ODxlavXo3S0lK89NJL95xz/vz5+OGHH4LGtm3bBq/Xi7q6Oq6aEBERUXgNy6uvvorPP/8cX331FRITE9Hd3Q0ASE5OhslkgsVigcViCdrHYDDAZrNh3rx5gbHnn38es2fPxs6dOxEfHx90DQwApKSkAMCocSIiIpqawmpYPvzwQwBAbm5u0Pgnn3yCF198UfE8XV1dQf9tRERERDSeCV3DokW8hoWIiGjyUfr5zWUOIiIi0jw2LERERKR5bFiIiIhI89iwEBERkeaxYSEiIiLNY8NCREREmseGhYiIiDSPDQsRERFpHhsWIiIi0rywbs2vZb/dsNfj8UT5SIiIiEip3z63Q914P2YaFq/XCwB8ujMREdEk5PV6kZycPOb2mHmW0MjICK5evYrExETodLpoH05M8Hg8yMzMxKVLl/h8pihhDqKL8Y8+5iC6HkT8RQRerxfp6enjPhg5ZlZY9Ho9MjIyon0YMSkpKYn/UEQZcxBdjH/0MQfRpXb8x1tZ+Q0vuiUiIiLNY8NCREREmseGhcZkNBpRU1MDo9EY7UOZspiD6GL8o485iC4txT9mLrolIiKi2MUVFiIiItI8NixERESkeWxYiIiISPPYsBAREZHmsWGZwn788UcUFRXBarUiKSkJK1euxNGjRwPbz5w5g/Xr1yMzMxMmkwkLFixAXV1dyHl9Ph8qKipgtVqRkJCAtWvX4vLly2qeyqQVKgcAsHnzZuTk5MBoNGLx4sWK5s3NzYVOpwt6FRcXq3AGk5ta8WcNKKckB11dXVizZg0SEhJgtVpRWVmJwcHBcedlDSijVvzVqAE2LFNYQUEB/H4/jhw5AqfTicWLF6OwsBDd3d0AAKfTidTUVHz22Wfo6OjAm2++ierqarz//vvjzrtlyxbs27cPDQ0NaGlpwc2bN1FYWIjh4eEHcVqTSqgcAHduW/3yyy9j3bp1Yc1dVlYGt9sdeNXX10f68Cc9teLPGlAuVA6Gh4dRUFCA/v5+tLS0oKGhAV9++SVee+21kHOzBkJTK/6q1IDQlHT9+nUBIMeOHQuMeTweASBNTU1j7ldeXi5PPfXUmNt7e3vFYDBIQ0NDYOzKlSui1+vl8OHDkTn4GBFuDmpqamTRokWK5n7yySdl8+bNETrS2KRW/FkDyinJwaFDh0Sv18uVK1cC7/niiy/EaDRKX1/fmHOzBkJTK/5q1QBXWKYoi8WCBQsW4NNPP0V/fz/8fj/q6+uRlpaGnJycMffr6+uD2Wwec7vT6cTQ0BBWrVoVGEtPT4fdbsd3330X0XOY7CaaA6V2794Nq9WKhQsXYuvWrYEnmtMdasWfNaCckhwcP34cdrsd6enpgf1Wr14Nn88Hp9M57vysgfGpFX+1aiBmHn5I4dHpdGhsbERRURESExOh1+uRlpaGw4cPIyUl5Z77HD9+HHv27MHBgwfHnLe7uxtxcXGYMWNG0HhaWlrQMjtNLAdKlZSUICsrCzabDe3t7aiursaZM2fQ2NgYmYOPAWrFnzWgnJIcdHd3Iy0tLWi/GTNmIC4ubtx4sgZCUyv+atUAV1hizPbt20ddaPb/r9OnT0NEUF5ejpkzZ6K5uRnff/89ioqKUFhYCLfbPWrejo4OFBUV4a233sIzzzwT9nGJCHQ6XSROUfPUykE4ysrKkJ+fD7vdjuLiYuzduxdNTU1obW2N0Flqlxbify+sgYnn4F5xCxVP1kB0438v91sDXGGJMQ6HI+SV8I888giOHDmCAwcOoKenJ/DI8A8++ACNjY3YtWsX3njjjcD7z507h7y8PJSVlWHbtm3jzm2z2TA4OIienp6g7vratWtYsWLFfZzZ5KFGDu7X0qVLYTAYcOHCBSxdujRi82pRtOPPGohsDmw2G06ePBm0b09PD4aGhkb95j8e1kAwNeOvVg2wYYkxVqsVVqs15Ptu3boFANDrgxfZ9Ho9RkZGAj93dHQgLy8PL7zwAnbs2BFy3pycHBgMBjQ2NuK5554DALjdbrS3t+Pdd98N51QmrUjnIBI6OjowNDSEWbNmRXReLYp2/FkDkc3B8uXLsWPHDrjd7sDf36+//hpGozGsa41YA6OpFX/VamDCl+vSpHb9+nWxWCzy7LPPisvlkvPnz8vWrVvFYDCIy+USEZH29nZJTU2VkpIScbvdgde1a9cC81y+fFnmzZsnJ0+eDIxt2rRJMjIypKmpSVpbWyUvL08WLVokfr//gZ+nlinJgYjIhQsXpK2tTV555RWZO3eutLW1SVtbm/h8PhEZnYOffvpJamtr5dSpU9LZ2SkHDx6U+fPny5IlS5iD/6FW/EVYA0opyYHf7xe73S5PP/20tLa2SlNTk2RkZIjD4QjMwxqYGLXiL6JODbBhmcJOnTolq1atErPZLImJibJs2TI5dOhQYHtNTY0AGPWaM2dO4D2dnZ0CQI4ePRoYu337tjgcDjGbzWIymaSwsFC6uroe4JlNHqFyIHLn65n3ykNnZ6eIjM5BV1eXPPHEE2I2myUuLk6ys7OlsrJSbty48YDPTvvUiL8IayAcSnJw8eJFKSgoEJPJJGazWRwOhwwMDAS2swYmTo34i6hTAzoRkYmvzxARERGpj98SIiIiIs1jw0JERESax4aFiIiINI8NCxEREWkeGxYiIiLSPDYsREREpHlsWIiIiEjz2LAQERGR5rFhISIiIs1jw0JERESax4aFiIiINI8NCxEREWnefwGf0Dh8RHWpHAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are your orig (faint) and squished shapes for clip no 1 out of 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 when ready 1\n"
     ]
    }
   ],
   "source": [
    "print(\"for some states like FL, we move in partial counties before running option 2\")\n",
    "vtdCP = tractCP.copy()\n",
    "#CODE TO SQUISH GEOMETRIES FROM CLIP LINES INTO THE MAP\n",
    "print(\"adding code here to 'push in' state panhandles ...\")\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "stateRows = trimDF[\"state\"].to_list()\n",
    "DFrow = stateRows.index(STATE)\n",
    "nClips = trimDF[\"nClipPoly\"][DFrow]\n",
    "nCuts  = trimDF[\"nCutPoly\"][DFrow]\n",
    "#Adding in here trimming ops\n",
    "toSquishUnitsList = list()\n",
    "toSquishGeomsList = list()  #combined list of geoms we will squish (over all squish operations)\n",
    "toSquishCountiesList = list()\n",
    "squishedShapesList = list()   #unit shapes after squishing, list over all squish operations\n",
    "if nClips > 0:\n",
    "    clipPoints = ast.literal_eval(trimDF[\"clipPoints\"][DFrow])   #sets of four points\n",
    "    farPoints  = ast.literal_eval(trimDF[\"farPoints\"][DFrow])    #pairs of points\n",
    "    newFarPoints  = ast.literal_eval(trimDF[\"newFarPoints\"][DFrow])   #pairs\n",
    "    cPts = [Point(clipPoints[i][0],clipPoints[i][1]) for i in range(len(clipPoints)) ]\n",
    "    fPts = [Point(farPoints[i][0],farPoints[i][1]) for i in range(len(farPoints)) ]\n",
    "    nfPts = [Point(newFarPoints[i][0],newFarPoints[i][1]) for i in range(len(newFarPoints)) ]\n",
    "    \n",
    "    for clipNo in range(nClips):\n",
    "        print(\"performing squish no\",clipNo+1,\"for state\",STATE)\n",
    "        n0,n1,n2,n3 = int(4*clipNo), int(4*clipNo+1), int(4*clipNo+2), int(4*clipNo+3)\n",
    "        clipPoly = Polygon([cPts[n0],cPts[n1],cPts[n2],cPts[n3] ] )\n",
    "        cutLine = LineString([cPts[n0],cPts[n1]] )\n",
    "        farLine = LineString([fPts[int(2*clipNo)],fPts[int(2*clipNo+1)] ] )\n",
    "        newFarLine = LineString([nfPts[int(2*clipNo)],nfPts[int(2*clipNo+1)] ])\n",
    "        \n",
    "        toSquishCounties = list()\n",
    "        toSquishUnits = list()\n",
    "        for c in range(nCounties):\n",
    "            if clipPoly.intersects(countyGeom[c]):\n",
    "                toSquishCounties.append(c)\n",
    "                if clipPoly.contains(countyGeom[c]):\n",
    "                    toSquishUnits = toSquishUnits + countyTractList[c]\n",
    "                else:\n",
    "                    for t in countyTractList[c]:\n",
    "                        if clipPoly.contains(vtdCP[t]):\n",
    "                            toSquishUnits.append(t)\n",
    "        print(\"clipPoly\",clipNo,\"intersects\",toSquishCounties,\"counties and a total of\",len(toSquishUnits),\"units\")\n",
    "        toSquishGeomUnion = vtdGeom[toSquishUnits[0]]\n",
    "        toSquishGeoms = list()\n",
    "        for t in toSquishUnits:\n",
    "            toSquishGeomUnion = toSquishGeomUnion.union(vtdGeom[t])\n",
    "            toSquishGeoms.append(vtdGeom[t])\n",
    "        unclippedGeom = MAP.difference(toSquishGeomUnion)\n",
    "        altCutLine = toSquishGeomUnion.buffer(0.001).intersection(unclippedGeom)\n",
    "        print(\"Here is the original cutLine and an alternate based on cut shapes\")\n",
    "        #plotPoly(MAP,0.1)\n",
    "        plotPoly(countyGeom[43])\n",
    "        plotPoly(countyGeom[42])\n",
    "        plotPoly(cutLine.buffer(0.01))\n",
    "        plotPoly(altCutLine,0.2)\n",
    "        plt.show()\n",
    "        # could use altCutLine for a more accurate squish at the squish-no_squish boundary ...\n",
    "        #newShapes = squish(clippedGeomList, altCutLine, farLine, newFarLine)\n",
    "        squishedShapes = squish(toSquishGeoms, cutLine, farLine, newFarLine) #..but may distort results\n",
    "        toSquishUnitsList.append(toSquishUnits)\n",
    "        toSquishGeomsList.append(toSquishGeoms)\n",
    "        toSquishCountiesList.append(toSquishCounties)\n",
    "        squishedShapesList.append(squishedShapes)\n",
    "        for geo in toSquishGeoms:\n",
    "            plotPoly(geo,0.1)\n",
    "            plotPoly(geo.centroid.buffer(0.004),0.1)\n",
    "        for geo in squishedShapes:\n",
    "            plotPoly(geo)\n",
    "            plotPoly(geo.centroid.buffer(0.008),0.4)\n",
    "        print(\"These are your orig (faint) and squished shapes for clip no\",clipNo+1,\"out of\",nClips)\n",
    "        plt.show()\n",
    "        pause = input(\"enter 1 when ready\")     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "e7e21005-dc00-49b1-9719-62e526a12b2e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POINT (-87.19171719578974 30.52313586297459)\n"
     ]
    }
   ],
   "source": [
    "print(tractCP[1111])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "73dbac1b-4bf3-480a-b028-6edf55a5447d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alternate simple approach for Florida Keys - translate all toward county CP\n",
      "I translated 28 vtds toward the center of county 43\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"alternate simple approach for Florida Keys - translate all toward county CP\")\n",
    "c = 43\n",
    "vtdGeom = tractGeom.copy()  #a reset\n",
    "nTranslated = 0\n",
    "newCP = Point(-81, 25.1)\n",
    "for v in countyTractList[c]:\n",
    "    if clipPoly.contains(tractCP[v]):\n",
    "        xDelta, yDelta = newCP.x - tractCP[v].x, newCP.y - tractCP[v].y\n",
    "        xOFF, yOFF = 0.95 * xDelta, 0.95 * yDelta\n",
    "        vtdGeom[v] = translate(vtdGeom[v],xoff= xOFF, yoff=yOFF)\n",
    "        nTranslated +=1\n",
    "print(\"I translated\",nTranslated,\"vtds toward the center of county\",c)\n",
    "hdCP = [vtdGeom[v].centroid for v in range(nTracts)]\n",
    "countyGeom[c] = vtdGeom[countyTractList[c][0]]\n",
    "for v in countyTractList[c]:\n",
    "    countyGeom[c] = countyGeom[c].union(vtdGeom[v])\n",
    "    plotPoly(hdCP[v].buffer(0.03))\n",
    "plotPoly(countyGeom[c])\n",
    "unsquishedMAP = MAP\n",
    "MAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "#plotPoly(MAP)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5dc3a1ca-5aa2-47f9-9685-83a516dcd1fc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "popHDlist = populatedTractList.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ecc512e1-cb48-4c0b-90f4-b90458ee23f6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here we compute the map's convex hull.  And here is a dot map of the (shifted) unit centerpoints\n",
      "working on HD center 0\n",
      "working on HD center 502\n",
      "working on HD center 1004\n",
      "working on HD center 1506\n",
      "working on HD center 2006\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note the MAP convex hull.  Overwrite with your own polygon if desired.\n"
     ]
    }
   ],
   "source": [
    "#OPTION 2 - draw polygonal HDs.  #REQUIRES CONSISTENT TREATMENT OF CCB'S  #For GA, MA, we leave CCB's in the map, draw ALL vtds\n",
    "#Can draw for each vtd.  Or, read in tract or blockgroup geometries, use those pops and unit centerpoints\n",
    "print(\"Here we compute the map's convex hull.  And here is a dot map of the (shifted) unit centerpoints\")\n",
    "convexMAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    convexMAP = convexMAP.union(countyGeom[c])\n",
    "#convexMAP = MAP.centroid\n",
    "#for c in range(nCounties):\n",
    "#    if c not in allFusedCounties:\n",
    "#        convexMAP = convexMAP.union(countyGeom[c])\n",
    "MAPhull = convexMAP.convex_hull.buffer(0.01*convexMAP.area**0.5)\n",
    "plotPoly(MAPhull)\n",
    "hdCP = [vtdGeom[v].centroid for v in range(nTracts)]\n",
    "popHDlist = list()\n",
    "for t in range(nTracts):\n",
    "    if tractPop[t] > 0:\n",
    "        popHDlist.append(t)\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD center\",t)\n",
    "    if hdCP[t].disjoint(convexMAP.convex_hull):\n",
    "        plotPoly(hdCP[t].buffer(0.03))\n",
    "        hdCP[t] = nearest_points(convexMAP.convex_hull,hdCP[t])[0]\n",
    "        plotCenter(\"m\",hdCP[t])\n",
    "    else:\n",
    "        plotPoly(hdCP[t].buffer(0.01))\n",
    "plotPoly(convexMAP)\n",
    "plotPoly(convexMAP.convex_hull)\n",
    "plotPoly(MAPhull)\n",
    "for c in allFusedCounties:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "plt.show()\n",
    "\n",
    "print(\"Note the MAP convex hull.  Overwrite with your own polygon if desired.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "650ae996-d3ca-422f-8ab0-3ffa3cf6eb67",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "nHDs = nTracts  #need to run this if we start option 2 from a vest list, not a previously run HD poly list\n",
    "HDcountyNo = countyNo.copy()\n",
    "populatedTractList = popHDlist.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "54f09a52-c279-4647-9860-0729d1912b4e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let us establish the (post-squish) point-point distances for all units, about 8 min for 4000-unit state\n",
      "working on unit-unit distances for unit 0 out of 2157 . Time = 0\n",
      "working on unit-unit distances for unit 401 out of 2157 . Time = 46\n",
      "working on unit-unit distances for unit 802 out of 2157 . Time = 83\n",
      "working on unit-unit distances for unit 1204 out of 2157 . Time = 111\n",
      "working on unit-unit distances for unit 1606 out of 2157 . Time = 129\n",
      "working on unit-unit distances for unit 2006 out of 2157 . Time = 138\n",
      "All done; total elapsed  138 seconds for MA with 8 districts to map\n"
     ]
    },
    {
     "data": {
      "image/png": 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GSVJKSorf+pSUFGdbQ0OD4uLiNHLkyF5rkpOTu3x+cnKyU3O50tJSZ/6M2+1Wenr6Ve8PAAAIXwNydZLL5fJ7bYzpsu5yl9d0V9/b56xatUper9dZ6urq+tFyAABgi6CGGI/HI0ldjpY0NjY6R2c8Ho/a29vV1NTUa82HH37Y5fM/+uijLkd5OsXHxysxMdFvQeidnjBxUL6Hy9IBIPoENcRkZGTI4/GosrLSWdfe3q6qqipNnz5dkpSdna3Y2Fi/mvr6ep08edKpycnJkdfr1euvv+7UHDlyRF6v16kBAADRLeCrk1paWvT22287r2tra1VTU6OkpCR94xvfUFFRkdasWaPx48dr/PjxWrNmjYYPH6758+dLktxutxYuXKjly5dr1KhRSkpK0ooVKzRp0iTnaqWJEydq9uzZWrRokZ588klJ0r333quCggKuTAIAAJL6EWKOHTumW265xXm9bNkySdKCBQu0ZcsWrVy5Um1tbXrggQfU1NSkqVOnateuXUpISHDes379esXExGju3Llqa2tTbm6utmzZoiFDhjg127dv19KlS52rmObMmdPjvWlgl3PF+0PdBABABAg4xMycOVPGmB63u1wulZSUqKSkpMeaoUOHasOGDdqwYUOPNUlJSdq2bVugzQMAAFGCZycBAAArEWJCrLcjVgAAoGeEGAy8EneoWwAAiECEGAAAYCVCTAh59tSEugkRbbButAcACA1CDAAAsBIhBgAAWIkQAwwSbvIHAMFFiAEAAFYixAAAACsRYkJkXPHOUDcBAACrEWLCXCjnUTCHAwAQzggx6NXaeQWhbgIAAN0ixAAAACsRYizEnX4BACDEAAAASxFiAACAlQgxAADASoQYAABgJUIMgMhX4g51CwAMAEIMAACwEiEmFPitEACAq0aIAQAAViLEAAAAKxFiEDKnJ0wMdRMAABYjxAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDILqtd3XhboJAIAoQYgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYq5GiTvULQAAIGoRYgAAgJUIMbAfR8QAICoRYgZBSUlJqJsAAEDEIcRgQKydVxDqJgAAIhwhBgAAWIkQAwAArESIAQAAViLEDJJzxftD3QSEG66qAoCrQogBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiEHfcXM2AEAYIcRcpXHFO0PdBAAAohIhBgAAWIkQAwAArESIscDaeQWhbgIAAGGHEAMAAKxEiAEAAFYixADoOy6zBxBGCDEAAMBKhBigPzgiAQAhR4gBAvDH+3eHugkAgP8gxCAgnj01oW4CAACSCDED6vJHEvBbPAAAwUOIAQAAVgp6iCkpKZHL5fJbPB6Ps90Yo5KSEqWlpWnYsGGaOXOmTp065fcZPp9PS5Ys0ejRozVixAjNmTNH586dC3ZTEQIlJSWhbgIAIEIMyJGY73znO6qvr3eWEydOONseffRRrVu3Ths3btTRo0fl8Xh022236cKFC05NUVGRysvLVVZWpgMHDqilpUUFBQXq6OgYiOYCAAALDUiIiYmJkcfjcZZrr71W0hdHYR577DGtXr1ad911l7KysrR161Z9+umnev755yVJXq9Xmzdv1tq1azVr1izdcMMN2rZtm06cOKFXX311IJqLQfLa7utC3QQAQAQZkBBz9uxZpaWlKSMjQz/96U/1zjvvSJJqa2vV0NCgvLw8pzY+Pl4zZszQwYMHJUnHjx/XxYsX/WrS0tKUlZXl1HTH5/OpubnZbwEAAJEr6CFm6tSpevbZZ/X3v/9dTz/9tBoaGjR9+nR98sknamhokCSlpKT4vSclJcXZ1tDQoLi4OI0cObLHmu6UlpbK7XY7S3p6epD3DAAAhJOgh5j8/HzdfffdmjRpkmbNmqWdO7+4zHjr1q1Ojcvl8nuPMabLustdqWbVqlXyer3OUldXdxV7AQAAwt2AX2I9YsQITZo0SWfPnnWuUrr8iEpjY6NzdMbj8ai9vV1NTU091nQnPj5eiYmJfgsAAIhcAx5ifD6fTp8+rdTUVGVkZMjj8aiystLZ3t7erqqqKk2fPl2SlJ2drdjYWL+a+vp6nTx50qkBAAAIeohZsWKFqqqqVFtbqyNHjugnP/mJmpubtWDBArlcLhUVFWnNmjUqLy/XyZMndc8992j48OGaP3++JMntdmvhwoVavny5XnvtNVVXV+vnP/+5c3oK4aHL/V54ICIAYJDFBPsDz507p5/97Gf6+OOPde2112ratGk6fPiwxo4dK0lauXKl2tra9MADD6ipqUlTp07Vrl27lJCQ4HzG+vXrFRMTo7lz56qtrU25ubnasmWLhgwZEuzmWmXS1kmSHgl1M3o0rninNudduQ4AgGAIeogpKyvrdbvL5VJJSUmvd24dOnSoNmzYoA0bNgS5dQAAIFLw7CRcFR5qCQAIFUIMAtZ559218wokdZ7mAgBgcBFiokyoAse44p0h+V4MLv6eAQwmQgyCjidVAwAGAyEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDGWOD1hYqibAABAWCHEAJchMAKAHQgxEYK75vbOs6cm1E0AAAQZIQYRiWc6AUDkI8QAAAArEWIAAICVCDFAmGF+EwD0DSEGUYMHUwJAZCHEIKqcK94f6iZ0K2jtKnEH53MAwAKEGKCP1s4rCHUT+mRc8c5QNwEABgUhBgAAWIkQA3+cjgAAWIIQA0QAbu4HIBoRYgAAgJUIMUAI8UwnAOg/QgwAALASIQbWsf2mdacnTAx1EwAgIhBiAACAlQgxQDd4fhEAhD9CDKIek2t7F66PagAAQgwiBmEEAKILIQaIUMGcAM3N9ACEI0IMIl53D0SMlCuErhRUOBUEIJIRYgDLdfd07dd2XxeClgDA4CLEAD0J8cMwmeMDAL0jxKBb4f6bfG+nSQI5hdLdqaZg61cY4WniAHBFhBiLMd8heAY6tIV7KAzEYAQ/AOgLQgwiWjBuWkfAAYDwRIhB1OKuvJGtuwnPACILIQZRJ5AfbuE6uTbUp3TCtV8ARBdCTJD09cZikXJ/kkHXzUTXYN+A7Vzxfn57BwCLEGIA24TJlUvBvCMwAPQHIQbog2BfCcZ8nMHHKTAg8hBigiiST0XwQ3fgXWn82Pj8okj+NwEg9AgxUc72e80wxwgAohchBgAAWIkQAwAArESIiWI23inWtjaH+n4uAy1Yz7ACgP4gxFiOiZO4EhsnBANAXxBiBkqY3MsjEOF+BVKkH9WwXW/jJ9zHFgA7EWLA0RxcNa4SAxAKhBjAUrbNDwKAYCPEAGHkSvNXIuG0DOELQLAQYoAwxzOKAKB7hBjAAjbNW+IZRcHDZHagd4QYIEKE0+Rajh4BGAyEGAAAYCVCTAQIp9/AEd5CeXqi1++28L5KAEKPEAOEsaBeyUNQABBhCDFAFAj0aF0gR2yC9VgDJgQDCBQhZoDZck8MTkkBAGxDiAEAAFYixASBLUdbgHBxrnh/qJvwJeYKAdYixAAIGU5jXhlzhYCeEWIGwGD8x8zRH0SKSHgeFIDQIMQACCtXc6opkKMWwbqqKlTC/ZEE/KKFwUCIARAS3T0P6qs/+IIRMnr6jM7v7jwKNK54p17bfZ1KSkq0dl7Bl+8rcfttC1a7AsVjHIDuEWKCzPbf7oBg++rp1b4eKbk8ZAQ6L6SnozmBHB1gvk7/MY8Hg4UQA6BbYXM6IMCrh8Km3QAGHCEGQI8i4TTG5ROHB3KfOBILDC5CDABYitM2iHZhH2Ief/xxZWRkaOjQocrOztb+/WF0kywAABAyYR1iXnjhBRUVFWn16tWqrq7WzTffrPz8fL333nuhbhoAhP1lzkCkC+sQs27dOi1cuFC//OUvNXHiRD322GNKT0/Xpk2bQt00AFGsr1dc9fd0z5WujArXGwSGa7v6itNz9okJdQN60t7eruPHj6u4uNhvfV5eng4ePNil3ufzyefzOa+9Xq8kqbm5eUDa19HWoWaf0SXfp2ptvSSfz6fYixdl2lvV0tF12wVfq9raL/ptu9Ta0mVbR5ury7bPLnbd1mouddnWl33taOvo/TN8pss209765f7+Z9tX9/dq+0J9+TsKRX9eoS/60u5w6+tLvk/7NE7Cra8vtbb0rd0D3J/Nzc1q69zW3OzsU3Nzs95/+KDa2i/6tbOv7b7c5Z/f+d2dvvrvuHP9hnv+r/JO1Crz+LGAvy9YOtr+0+7/9PVA/f87UPr79zVYSktL9bUz/6v4ry/W/3z/v3T4X+eU9dlmbbx1pQ4d/Kmzbcb+5cr8SYPftv/32Qzt9F702/ZZbqoW/mOn37YFy2PUcua//ba9+K/1yjtR67ftT6ZQ43dv0v/57+lB38/OvwNjzJWLTZh6//33jSTzj3/8w2/9b3/7W3P99dd3qX/44YeNJBYWFhYWFpYIWOrq6q6YFcL2SEwnl8vl99oY02WdJK1atUrLli1zXl+6dEn//ve/NWrUqG7r+6q5uVnp6emqq6tTYmJivz8nmtBngaPPAkN/BY4+Cxx9Fphg9ZcxRhcuXFBaWtoVa8M2xIwePVpDhgxRQ0OD3/rGxkalpKR0qY+Pj1d8fLzfuq9//etBa09iYiKDOED0WeDos8DQX4GjzwJHnwUmGP3ldrv7VBe2E3vj4uKUnZ2tyspKv/WVlZWaPj345+AAAIBdwvZIjCQtW7ZMhYWFmjJlinJycvTUU0/pvffe0/333x/qpgEAgBAL6xAzb948ffLJJ/rNb36j+vp6ZWVl6ZVXXtHYsWMHrQ3x8fF6+OGHu5yqQs/os8DRZ4GhvwJHnwWOPgtMKPrLZUxfrmECAAAIL2E7JwYAAKA3hBgAAGAlQgwAALASIQYAAFiJECPp8ccfV0ZGhoYOHars7Gzt37+/1/qqqiplZ2dr6NCh+uY3v6knnnhikFoaPgLps71798rlcnVZ3nrrrUFscejs27dPd9xxh9LS0uRyufTSSy9d8T3RPsYC7bNoH2OlpaW68cYblZCQoOTkZN155506c+bMFd8XzeOsP30WzeNs06ZN+u53v+vcyC4nJ0d/+9vfen3PYIyvqA8xL7zwgoqKirR69WpVV1fr5ptvVn5+vt57771u62tra/XDH/5QN998s6qrq/XQQw9p6dKl2rFjxyC3PHQC7bNOZ86cUX19vbOMHz9+kFocWq2trZo8ebI2btzYp3rGWOB91ilax1hVVZUefPBBHT58WJWVlfr888+Vl5en1tbWHt8T7eOsP33WKRrH2ZgxY/TII4/o2LFjOnbsmG699Vb96Ec/0qlTp7qtH7TxFZSnNVrs+9//vrn//vv91k2YMMEUFxd3W79y5UozYcIEv3X33XefmTZt2oC1MdwE2md79uwxkkxTU9MgtC68STLl5eW91jDG/PWlzxhj/hobG40kU1VV1WMN48xfX/qMceZv5MiR5k9/+lO32wZrfEX1kZj29nYdP35ceXl5fuvz8vJ08ODBbt9z6NChLvW33367jh07posXLw5YW8NFf/qs0w033KDU1FTl5uZqz549A9lMq0X7GLsajLEveL1eSVJSUlKPNYwzf33ps07RPs46OjpUVlam1tZW5eTkdFszWOMrqkPMxx9/rI6Oji4PlExJSeny4MlODQ0N3dZ//vnn+vjjjwesreGiP32Wmpqqp556Sjt27NCLL76ozMxM5ebmat++fYPRZOtE+xjrD8bYl4wxWrZsmW666SZlZWX1WMc4+1Jf+yzax9mJEyf0ta99TfHx8br//vtVXl6ub3/7293WDtb4CuvHDgwWl8vl99oY02Xdleq7Wx/JAumzzMxMZWZmOq9zcnJUV1en3//+9/rBD34woO20FWMsMIyxLy1evFhvvPGGDhw4cMVaxtkX+tpn0T7OMjMzVVNTo/Pnz2vHjh1asGCBqqqqegwygzG+ovpIzOjRozVkyJAuRxAaGxu7JMhOHo+n2/qYmBiNGjVqwNoaLvrTZ92ZNm2azp49G+zmRYRoH2PBEo1jbMmSJXr55Ze1Z88ejRkzptdaxtkXAumz7kTTOIuLi9O3vvUtTZkyRaWlpZo8ebL+8Ic/dFs7WOMrqkNMXFycsrOzVVlZ6be+srJS06dP7/Y9OTk5Xep37dqlKVOmKDY2dsDaGi7602fdqa6uVmpqarCbFxGifYwFSzSNMWOMFi9erBdffFG7d+9WRkbGFd8T7eOsP33WnWgaZ5czxsjn83W7bdDGV1CnCVuorKzMxMbGms2bN5s333zTFBUVmREjRph3333XGGNMcXGxKSwsdOrfeecdM3z4cPPrX//avPnmm2bz5s0mNjbW/OUvfwnVLgy6QPts/fr1pry83Pzzn/80J0+eNMXFxUaS2bFjR6h2YVBduHDBVFdXm+rqaiPJrFu3zlRXV5t//etfxhjGWHcC7bNoH2O/+tWvjNvtNnv37jX19fXO8umnnzo1jDN//emzaB5nq1atMvv27TO1tbXmjTfeMA899JC55pprzK5du4wxoRtfUR9ijDHmj3/8oxk7dqyJi4sz3/ve9/wusVuwYIGZMWOGX/3evXvNDTfcYOLi4sy4cePMpk2bBrnFoRdIn/3ud78z1113nRk6dKgZOXKkuemmm8zOnTtD0OrQ6Lws8/JlwYIFxhjGWHcC7bNoH2Pd9ZUk88wzzzg1jDN//emzaB5nv/jFL5z/86+99lqTm5vrBBhjQje+XMb8Z6YNAACARaJ6TgwAALAXIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAVvr/L4MBCFwHxOAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 ....... ##########  THIS IS FOR TRACT - TRACT; SEE LATER FOR unit - unit ...\n",
    "print(\"Let us establish the (post-squish) point-point distances for all units, about 8 min for 4000-unit state\")\n",
    "#NOTE: FOR LARGE STATES, RESTRICT THE SEARCH TO COUNTIES WITHIN A 3/SQRT(nDistrict) DIAMETER OF THE HOME UNIT\n",
    "uuDist = [[int(maxD+1) for t in range(nHDs)] for t in range(nHDs)] #default = too big to capture\n",
    "closeCountyList = [list() for c in range(nCounties)]\n",
    "closeCountyDist = maxD   #min(maxD,3./float(nDistricts)**0.5 * maxD )  #use above maxD for these counties as well\n",
    "xScaleSquared = xScale*xScale\n",
    "if nDistricts < 10: #closeCountyDist >= maxD:  #small state\n",
    "    closeCountyList = [[i for i in range(nCounties)] for c in range(nCounties)]  #all counties are close enough\n",
    "else:\n",
    "    for c in uncutCountyList:\n",
    "        closeCountyList[c].append(c)\n",
    "        for cc in range(c+1,nCounties):\n",
    "            if cc in uncutCountyList:\n",
    "                if cc in neighborCountyList[c]:\n",
    "                    closeCountyList[c].append(cc)\n",
    "                    closeCountyList[cc].append(c)\n",
    "                else:    \n",
    "                    dist = getLongDist(countyCP[c], countyCP[cc],xScale)\n",
    "                    if dist < closeCountyDist:\n",
    "                        closeCountyList[c].append(cc)\n",
    "                        closeCountyList[cc].append(c)\n",
    "startTime = time.time()\n",
    "#populatedTractList = popHDlist.copy()  #nomenclature\n",
    "for i,t in enumerate(populatedTractList):\n",
    "    if i %400 == 0:\n",
    "        print(\"working on unit-unit distances for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime))\n",
    "    uuDist[t][t] == 0.\n",
    "    for tt in populatedTractList:\n",
    "        if tt > t and (HDcountyNo[tt] in closeCountyList[HDcountyNo[t]] or HDcountyNo[t] == HDcountyNo[tt]):  #time-saver; do the triangle matrix\n",
    "                dist = getLongDist(hdCP[t], hdCP[tt],xScale)\n",
    "                uuDist[t][tt], uuDist[tt][t] = dist, dist\n",
    "print(\"All done; total elapsed \",int(time.time()-startTime),\"seconds for\",STATE,\"with\",nDistricts,\"districts to map\" )\n",
    "plt.hist(uuDist)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bc91a02a-52b2-4ac3-afc2-7f1650e32922",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "similar to above, but for angles\n",
      "working on unit-unit angles for unit 0 out of 2157 . Time = 0\n",
      "working on unit-unit angles for unit 200 out of 2157 . Time = 12\n",
      "working on unit-unit angles for unit 401 out of 2157 . Time = 23\n",
      "working on unit-unit angles for unit 602 out of 2157 . Time = 33\n",
      "working on unit-unit angles for unit 802 out of 2157 . Time = 42\n",
      "working on unit-unit angles for unit 1004 out of 2157 . Time = 50\n",
      "working on unit-unit angles for unit 1204 out of 2157 . Time = 56\n",
      "working on unit-unit angles for unit 1405 out of 2157 . Time = 61\n",
      "working on unit-unit angles for unit 1606 out of 2157 . Time = 65\n",
      "working on unit-unit angles for unit 1806 out of 2157 . Time = 68\n",
      "working on unit-unit angles for unit 2006 out of 2157 . Time = 69\n",
      "All done with angles; total elapsed seconds = 70\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 \n",
    "print(\"similar to above, but for angles\")  #convention is angle[a][b] is from a to b\n",
    "uuAngle = [[0. for t in range(nHDs)] for t in range(nHDs)]\n",
    "\n",
    "pi = 3.141592653\n",
    "twoPi = pi*2.\n",
    "startTime = time.time()\n",
    "for i, t in enumerate(populatedTractList):\n",
    "    if i %200 == 0:\n",
    "        print(\"working on unit-unit angles for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime)  )\n",
    "    for tt in populatedTractList:\n",
    "        if tt > t and (HDcountyNo[tt] in closeCountyList[HDcountyNo[t]] or HDcountyNo[t] == HDcountyNo[tt]):  #time-saver; do the triangle matrix\n",
    "            angLE = math.atan2(hdCP[tt].y - hdCP[t].y, xScale * (hdCP[tt].x - hdCP[t].x) )\n",
    "            uuAngle[t][tt], uuAngle[tt][t] = angLE % twoPi, (angLE + pi) % twoPi\n",
    "print(\"All done with angles; total elapsed seconds =\",int(time.time()-startTime) )\n",
    "plt.hist(uuAngle)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "83cf363c-a1db-4c15-924a-df668c3507a2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "781460.625 8 6251685.0 8.0 7029917.0\n"
     ]
    }
   ],
   "source": [
    "print(aDP, nDistricts, statePop, statePop/aDP, trueStatePop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4e4690c9-a317-4944-b5fa-8ba6af7c26e9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6251685.0 7029917 0.9999999999999999\n"
     ]
    }
   ],
   "source": [
    "HDweight = [0 for t in range(nHDs)]\n",
    "for t in populatedTractList:\n",
    "    HDweight[t] = tractPop[t] / trueStatePop #skipping the cutList tracts\n",
    "print(statePop,np.sum([tractPop[t] for t in populatedTractList]), np.sum(HDweight)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3214fdc5-d104-4164-9478-7ab097ce01e1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\n",
      "For each Home District, we will consider a wedge as one-from-full when it is within 2325.127 of targetWedgePop\n",
      "I am working on HD number 0 of 2157 HDs.  Elapsed sec = 0\n",
      "I am working on HD number 401 of 2157 HDs.  Elapsed sec = 42\n",
      "I am working on HD number 802 of 2157 HDs.  Elapsed sec = 87\n",
      "I am working on HD number 1204 of 2157 HDs.  Elapsed sec = 140\n",
      "I am working on HD number 1606 of 2157 HDs.  Elapsed sec = 182\n",
      "I am working on HD number 2006 of 2157 HDs.  Elapsed sec = 238\n",
      "257.443 seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of unit usage.\n",
      "unit avg and sd usage are 1.00017 0.13425\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are your HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 .......\n",
    "# BELOW modified Dec23 to use inter-unit distances and angles, (counties still wedgeIntersxn) otherwise similar to HD2\n",
    "# --THIS ESSENTIALLY TAKES THE ORIGINAL TRACT-BASED HD centerpoints, and redraws HD2 districts after convexifying corners and convex_hull\n",
    "print(\"Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\") \n",
    "#this is a hybrid of HD2 and the organic code, in that all wedge tracts must be in a chain of neighboring counties\n",
    "#General sequence: draw 4 equi-angle wedges with random starting orientation.  If not all can fill a quarter aDP ...\n",
    "# ... find the closest map boundary point to the tract center point in the shortest wedge.  Orient the 0th wedge to face this point\n",
    "# ... determine the \"maxWedgePop\" for this short wedge and the other three wedges extended past the boundary\n",
    "# ...  if this results in only one short wedge, or opposing short wedges, adjust wedge angles\n",
    "# Regardless of whether we re-oriented or adjusted angles, compute each wedgePop for infinite radius\n",
    "#   ... then adjust other targetWedgePops if some found to be short.\n",
    "#   ... for non-shorted wedges, use bisection method to adjust radius to meet targetWedgePop\n",
    "#   ... once total HDpop within tolerance, add/subtract a tract fraction to fulfill HDpop\n",
    "#  create a list of fully used tracts per HD, save the tract# of the split tract and its fractional use\n",
    "#  Compute tractUse across all HDs at the end from the tractUsageLists.  Defer patching and partisan calcs to another code\n",
    "\n",
    "pi=3.1415926536\n",
    "#MAPhull = MAP.convex_hull #used for exitAngle calc.  Defined in an above block to allow user modification\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2.*pi / nWedges\n",
    "angle, angle2, wedgePoly = [0.]*nWedges, [0.]*nWedges, [dummyPoly]*nWedges  #start and stop angle of each wedge\n",
    "pt1, pt2, pt3 = [Point(0,0)]*nWedges, [Point(0,0)]*nWedges, [Point(0,0)]*nWedges #these four define the polygon wedge for a growing home district\n",
    "tractUse = [0.] * nHDs  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "HDtractList = [list()]*nHDs\n",
    "splitTractNo = [-999]*nHDs  #the tract that is only partially used to finish each HD\n",
    "splitTractUse = [0.]*nHDs  #and this is what fraction of the split tract is included\n",
    "HDvPop = [0.]*nHDs\n",
    "#HDweight = [tractPop[t] / (statePop - excludedPop) for t in range(nTracts)]  #relative weight of each drawn HD\n",
    "HDPoly1 = [dummyPoly]*nHDs  #final 4-wedge shape, unionized from blockgroups / tracts\n",
    "HDarea = [0.]*nHDs  #geographical area of each home district (after boundary trim)\n",
    "HDradius = [[0.]*nWedges for t in range(nHDs)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nHDs)] #final starting angle for each HD wedge\n",
    "angle0 = [-999.] * nHDs  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "avgTractPop = statePop/len(populatedTractList)\n",
    "tolerPops = [0.8 * avgTractPop, 0.5 * np.max(tractPop)]  #threshold gap before adding one more unit to a wedge \n",
    "tolerPop = tolerPops[0]\n",
    "print(\"For each Home District, we will consider a wedge as one-from-full when it is within\",r3(tolerPops[0]),\"of targetWedgePop\")\n",
    "tractPrintInterval = 400  #for tracking progress\n",
    "levelL = 0.36 #sqrt(pop) for angle=std  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "maxAngleRatio = 1.9  #for tightening tract weighting near boundaries  #HD2\n",
    "maxAngle = 1.8*pi/2. # limit to avoid wedges too close to half a pie\n",
    "minAdjRatio = 0.5  #min ratio of pop of adjacent wedge (to min wedge) to targetWedgePop to not consider this a corner HD\n",
    "maxUncap = 0.5 #the max ratio of uncaptured pop in a maxWedge vs. target wedge pop that we will not stretch to include (7/23)\n",
    "oppWt = 0.0    #the fraction of gap between normal targetWedgePop and the minWedgePop that we allow the opposite wedge to add to tgtPop = minWedgePop\n",
    "maxWedgePop = [0.] * nWedges  #wedge pop if we explode wedge to maximum radius\n",
    "printDebug = \"no\"\n",
    "codeStartTime = time.time()\n",
    "hdCPpop = [HDweight[t] * statePop for t in range(nHDs)]\n",
    "countyHDlist = [list() for c in range(nCounties)]\n",
    "for t in populatedTractList:\n",
    "    countyHDlist[HDcountyNo[t]].append(t)\n",
    "\n",
    "for kkkj, t in enumerate(populatedTractList):  #range(nTracts) : #loop on each tract. \n",
    "    hC = HDcountyNo[t]     #this tract's home county\n",
    "    HDtractList[t] = [t] #seed the list of tracts in this HD\n",
    "    wedgeList = [list() for w in range(nWedges)]   #list of each wedge's tracts, excluding home tract\n",
    "    barredList = list()  #list of wedge numbers for which we are barred from altering their targetWedgePops\n",
    "    if (kkkj % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on HD number {0} of {1} HDs.  Elapsed sec = {2}\".format(t,nHDs,int(time.time()-codeStartTime) ) )  \n",
    "    nActiveWedges = nWedges #active wedges have not run over boundary\n",
    "    wedgePop = [0.]*nWedges\n",
    "    targetWedgePop = [aDP / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    \n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to the midangle of our starting wedge\n",
    "    wedgeAngle = [avgWedgeAngle for w in range(nWedges) ] #reset to equi-angle when starting each tract\n",
    "    for nW in range(nWedges-1):\n",
    "        wedgeAngle[nW] += random.uniform(-0.0001,0.0001)  #this wiggle solves a later indexing ambiguity for corner tracts\n",
    "    wedgeAngle[nWedges-1] = 2.*pi - (np.sum(wedgeAngle) - wedgeAngle[nWedges-1] ) #squaring up to 2pi total\n",
    "    startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "    for nW in range(1,nWedges):\n",
    "        startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "    endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "    #  TRIAL LOOP TO SEE WHICH WEDGES CAN FILL TO TARGET POP w/equiangle wedges\n",
    "    maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]    \n",
    "    maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "    willFill = [0] * nWedges #would this wedge meet its target with infinite radius?\n",
    "    for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "        iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "        #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList) \n",
    "        nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                neighborCountyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "        maxWedgePop[nW] += (iCP + nonCP)\n",
    "        wedgeList[nW] = Li + Ln\n",
    "        if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "            willFill[nW] = 1\n",
    "     \n",
    "    if np.sum(willFill) < nWedges:  #at least one wedge couldn't reach its wedgePop target (went over boundary) ...\n",
    "        minW = maxWedgePop.index(np.min(maxWedgePop)) #.. so we will orient the 0th wedge to face the shortest wedgePop's closest boundary\n",
    "        exitAngle = getExitAngle(hdCP[t],maxWedgePoly[minW], MAPhull, startAngle[minW], endAngle[minW])\n",
    "        angle0[t] = exitAngle - 0.5*(2.*pi / nWedges)  #Now reset all angles based on new starting orientation.  All wedge angles still equal\n",
    "        startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "        for nW in range(1,nWedges):\n",
    "            startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "        endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "        maxWedgePoly = [buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]\n",
    "        \n",
    "        willFill = [0]*nWedges\n",
    "        #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation (wedge angles still constant)\n",
    "        maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop        \n",
    "        for nW in range(nWedges):   #... then add in-county and nonCounty wedge Pop from contiguous chain of counties\n",
    "            iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "            #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "            nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                    neighborCountyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "            maxWedgePop[nW] += (iCP + nonCP)\n",
    "            wedgeList[nW] = Li + Ln\n",
    "            if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                willFill[nW] = 1       \n",
    "    \n",
    "    if np.sum(willFill) < nWedges:  #check if we need to modify wedge angles after possible re-orientation.  \n",
    "        # If so, re-draw maxWedgePoly's, re-calc maxWedgePops\n",
    "        nUnfilledWedges = nWedges - np.sum(willFill)\n",
    "        isChange, newInclA = getNewAngles(maxWedgePop, targetWedgePop, aDP, levelL, minAdjRatio, maxAngle, maxAngleRatio, nUnfilledWedges )\n",
    "        if isChange: #no longer equi-angle\n",
    "            newStartAngle = [0.5*(startAngle[nW]+endAngle[nW]) - 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            newEndAngle =   [0.5*(startAngle[nW]+endAngle[nW]) + 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            startAngle, endAngle, wedgeAngle = newStartAngle.copy(), newEndAngle.copy(), newInclA.copy()\n",
    "            maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], \n",
    "                                        maxD/math.cos(0.5*wedgeAngle[nW]), xScale ) for nW in range(nWedges) ]\n",
    "            willFill = [0]*nWedges\n",
    "            #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation and new wedge angles\n",
    "            maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "            for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "                iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])                \n",
    "                #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "                nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                        neighborCountyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "                maxWedgePop[nW] += (iCP + nonCP)\n",
    "                wedgeList[nW] = Li + Ln\n",
    "            minW = wedgeAngle.index(np.max(wedgeAngle)) #should be 0th wedge, but playing it safe.  This is the 1st wide-angle wedge ..\n",
    "            oppW = int(int(minW+nWedges/2)%nWedges)   #and its opposite\n",
    "            if maxWedgePop[minW] > maxWedgePop[oppW] and maxWedgePop[oppW] < aDP / nWedges:  #minW and oppW are flipped after inclA adjmt\n",
    "                oppW = minW\n",
    "                minW = int(int(minW+nWedges/2)%nWedges)\n",
    "            if maxWedgePop[minW] < targetWedgePop[oppW]:  #we need to constrain oppW in this HD2 implementation; redistribute tgt to adjacents\n",
    "                maxNonOppW = np.sum(maxWedgePop) - maxWedgePop[oppW] #...but only if other wedges can pick up slack; need to check\n",
    "                minOppWedgePop = aDP - maxNonOppW  #cannot set oppW target below this or we won't reach aDP across all wedges\n",
    "                barredList = [oppW]\n",
    "                targetWedgePop[oppW] = max(minOppWedgePop, maxWedgePop[minW] + oppWt * (targetWedgePop[oppW] - maxWedgePop[minW]) )\n",
    "                tWPgap = aDP / float(nWedges) - targetWedgePop[oppW] \n",
    "                for nW in range(nWedges):\n",
    "                    if nW != minW and nW != oppW:\n",
    "                        targetWedgePop[nW] += tWPgap/float(nWedges - 2.)  #if oppW target is limited, allot to adjacent wedges\n",
    "            for nW in range(nWedges):\n",
    "                if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                    willFill[nW] = 1  \n",
    "    \n",
    "    if abs(np.sum(targetWedgePop) / aDP - 1.) > 0.001:\n",
    "        raise Exception(\"FAIL! Our target wedgepops\",targetWedgePop, np.sum(targetWedgePop),\"no longer add up to\",aDP)\n",
    "    \n",
    "    if np.sum(willFill) == nWedges:  #all wedges will fill.  Will any leave a small near-boundary remnant?  If so, pick up smallest remnant\n",
    "        remnantWedgePopFrac = [ ( maxWedgePop[w] - targetWedgePop[w] )/targetWedgePop[w] for w in range(nWedges) ]\n",
    "        if np.min(remnantWedgePopFrac) < maxUncap :  #lessen tWP's of *adjacent* wedges, then increase this wedge's target --> max\n",
    "            minW = remnantWedgePopFrac.index(np.min(remnantWedgePopFrac))\n",
    "            oppW = int((minW+nWedges/2)%nWedges)\n",
    "            #print(\"filling to boundary for tract,wedge, tWP, mWP =\",t,minW, r3(targetWedgePop[minW]), maxWedgePop[minW])\n",
    "            for nW in range(nWedges):\n",
    "                if nW != minW and nW != oppW:\n",
    "                    targetWedgePop[nW] -= (remnantWedgePopFrac[minW] * targetWedgePop[minW] ) / (nWedges - 2.)\n",
    "            targetWedgePop[minW] = maxWedgePop[minW]\n",
    "            #       OK, READY TO SOLVE FOR NON-FILLED WEDGE SIZES once we adjust targets for unfilled wedges\n",
    "    #print(t,\" prior to rebalanceTWP call, mWPs, tWPs are\",maxWedgePop, targetWedgePop)\n",
    "    targetWedgePop, willFill = rebalanceTWPs(maxWedgePop, targetWedgePop, barredList)\n",
    "    #if np.max(targetWedgePop) - np.min(targetWedgePop) > 0.02 * aDP: #debug\n",
    "    #    print(\"for tract\",t,\",  After rebalancing but before tweaks for blocky pop adds: willFill, targetWedgePops and maxWedgePops are ...\")\n",
    "    #    for w in range(nWedges):\n",
    "    #        print(w, willFill[w],r3(targetWedgePop[w]),int(maxWedgePop[w]) )\n",
    "    for w in range(nWedges):  #WHEW!  WE CAN FINALLY ASSIGN ALL WEDGES' POPS AND TRACTLISTS\n",
    "        loop = 0\n",
    "        if willFill[w] == 0:  #wedge is maxed out\n",
    "            wedgePop[w] = maxWedgePop[w]\n",
    "            #wedgeList[w] = ...  #we already captured this list = maxWedge list\n",
    "            HDradius[t][w] = maxD/math.cos(0.5*wedgeAngle[w])\n",
    "        else:  #need to solve for wedge radius to meet targetPop.  Use bisection as scipy minimize no faster\n",
    "            HDcpWP = hdCPpop[t] * wedgeAngle[w]/(2.*pi)\n",
    "            nonHDcpTWP = targetWedgePop[w] - HDcpWP\n",
    "            #wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeB(nontractTWP,t,hC,maxWedgePoly[w],tolerPop,tractCP, tractPop,\n",
    "            #                                                        countyNo,countyTractList,countyGeom, neighborCountyList,uuDist[t])\n",
    "            wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeC(nonHDcpTWP,t,hC, maxWedgePoly[w],startAngle[w], endAngle[w], tolerPop,\n",
    "                                                                    hdCP, hdCPpop,HDcountyNo, countyHDlist,countyGeom,\n",
    "                                                                    neighborCountyList,uuDist[t],uuAngle[t])\n",
    "            popGap = nonHDcpTWP - wedgePop[w]  #gap between target and solved wedge pop; can be negative\n",
    "            notYetAdjusted, nextW = True, w+1  #looking to adjust a later wedge's target pop to minimize drift in total HD pop\n",
    "            while notYetAdjusted and nextW < nWedges:\n",
    "                if willFill[nextW] == 1 and maxWedgePop[nextW] > targetWedgePop[nextW] + popGap :  #can accommodate\n",
    "                    targetWedgePop[nextW] += popGap\n",
    "                    notYetAdjusted = False\n",
    "                nextW +=1          \n",
    "\n",
    "        HDangle[t][w] = startAngle[w]\n",
    "    #print(t,\"tract. After TWP adjustment, targetWedgePops are\",targetWedgePop)\n",
    "    HDtractList[t] = [t]\n",
    "    totPop = hdCPpop[t]\n",
    "    for nW in range(nWedges):\n",
    "        for tt in wedgeList[nW]:\n",
    "            HDtractList[t].append(tt)  #building the list of tracts in the Home District  (we'll keep up with below changes)\n",
    "            totPop += hdCPpop[tt]\n",
    "    offsetPop = totPop - aDP  #we have solved for all wedge radii to get each within one tractPop of target ... \n",
    "    HDvPop[t] = totPop\n",
    "    #if offsetPop > 0:   #... now include a splitPoly to nail the avg District Pop\n",
    "    #    isOver = True  #we slightly overshot.  ID the farthest-away tract for split-jettison\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], hdCPpop, neighborList)\n",
    "    #    HDvPop[t] = totPop - (1. - splitTractUse[t]) * tractPop[splitTractNo[t]]\n",
    "    #if offsetPop < 0:\n",
    "    #    isOver = False  #we have undershot.  Pick up the nearest contiguous tract that can be split to fill the gap\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], tractPop, neighborList)\n",
    "    #    HDtractList[t].append(splitTractNo[t])\n",
    "    #    HDvPop[t] = totPop + splitTractUse[t]*tractPop[splitTractNo[t]]       \n",
    "    #if abs(1. - HDvPop[t]/aDP) > 0.005 :  #something went wrong with pop assignation for this HD\n",
    "    #    print(\"WARNING! Total Home District pop was\",HDvPop[t],\"vs target\",aDP,\"for tract\",t)   ##### hdCP topology not yet known; skip partial\n",
    "        \n",
    "    HDPoly1[t] = dummyPoly  #tractGeom[t] #skip constructing the HD poly shape.  Do compute area of the Home District, including final partial\n",
    "    HDarea[t] = 0.\n",
    "    for tt in HDtractList[t]:\n",
    "        if tt == splitTractNo[t]:\n",
    "            tractUse[tt] += nDistricts * HDweight[t] * splitTractUse[t] \n",
    "            #HDarea[t]   += tractArea[tt] * splitTractUse[t]  #these are original (nonsquished) areas\n",
    "            \n",
    "        else:\n",
    "            tractUse[tt] += nDistricts * HDweight[t]\n",
    "            #HDarea[t]    += tractArea[tt]\n",
    "            #HDPoly1[t] = HDPoly1[t].union(tractGeom[tt])\n",
    "    HDPoly1[t] = buildPoly(hdCP[t],HDradius[t], HDangle[t] )\n",
    "    #HDarea[t] = HDPoly1[t].area + splitTractUse[t] * tractArea[splitTractNo[t]]  \n",
    "    #if splitTractUse[t] > 0.5:\n",
    "    #    HDPoly1[t] = HDPoly1[t].union(tractGeom[splitTractNo[t]])\n",
    "                          \n",
    "    #ALL DONE ESTABLISHING THE FINAL HDTRACTLIST.  FINALIZE STATS\n",
    "totalTime = time.time() - codeStartTime\n",
    "print(r3(totalTime),\"seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of unit usage.\")\n",
    "n_bins=50\n",
    "popTractUse, plotWts = [tractUse[t] for t in populatedTractList], [HDweight[t] for t in populatedTractList]\n",
    "\n",
    "origHDuseAvg, origHDuseSD = getWeightedAvgAndSD(tractUse,HDweight)\n",
    "print(\"unit avg and sd usage are\",r5(origHDuseAvg), r5(origHDuseSD) )\n",
    "\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "ax.hist(popTractUse, bins=n_bins, weights=plotWts)\n",
    "plt.show()\n",
    "HDvPop = [np.sum([hdCPpop[tt] for tt in HDtractList[t]]) for t in range(nHDs)]\n",
    "plt.scatter([hdCPpop[t] for t in populatedTractList],[HDvPop[t] for t in populatedTractList] )\n",
    "print(\"here are your HD pops\")\n",
    "plt.show()\n",
    "origHDHDtractList = [HDtractList[t].copy() for t in range(nHDs)] #save for posterity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "aec0cf55-112b-4eba-93c6-9cf1d1b87aae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now we will write the polygon-based results to a file\n"
     ]
    }
   ],
   "source": [
    "#last block of option 2 (i.e. generating polygonal HDs from scratch)\n",
    "print(\"Now we will write the polygon-based results to a file\")\n",
    "tractNo = [t for t in range(nHDs) ]\n",
    "HDangle0 = [HDangle[t][0] for t in range(nHDs) ]\n",
    "HDangle1 = [HDangle[t][1] for t in range(nHDs) ]\n",
    "HDangle2 = [HDangle[t][2] for t in range(nHDs) ]\n",
    "HDangle3 = [HDangle[t][3] for t in range(nHDs) ]\n",
    "HDradius0 = [HDradius[t][0] for t in range(nHDs)]\n",
    "HDradius1 = [HDradius[t][1] for t in range(nHDs)]\n",
    "HDradius2 = [HDradius[t][2] for t in range(nHDs)]\n",
    "HDradius3 = [HDradius[t][3] for t in range(nHDs)]\n",
    "hdCPx, hdCPy =   [hdCP[t].x for t in range(nHDs)], [hdCP[t].y for t in range(nHDs)]\n",
    "\n",
    "\n",
    "paramList = [\"STATE\",\"statePop\",\"nDistricts\",\"nHDs\",\"nWedges\",\"popn-toler\",\"levelL\", \"maxAngleRatio\",\n",
    "             \"maxAngle\",\"minAdjRatio\",\"maxUncap\", \"oppWt\", \"calc sec\",\"xScale\",\"outputs...\"]\n",
    "paramValues = [STATE,statePop, nDistricts, nHDs, nWedges, tolerPop, levelL, maxAngleRatio, maxAngle, minAdjRatio, maxUncap,\n",
    "               oppWt, totalTime, xScale, -777]\n",
    "for i in range(nHDs-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "HDdf = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"countyNo\":HDcountyNo,\n",
    "                    \"tractNo\":tractNo,\"centroid x\":hdCPx,\"centroid y\":hdCPy,\"tractPop\":hdCPpop,\n",
    "                    \"HDweight\":HDweight,\"HD-pop\":HDvPop,\"HDarea\":HDarea,\n",
    "                    \"startAngle\":angle0,\"HDangle0\":HDangle0,\"HDangle1\":HDangle1,\"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\n",
    "                    \"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\"HDradius2\":HDradius2, \"HDradius3\":HDradius3,\"tractUse\":tractUse,\n",
    "                    \"splitTractNo\":splitTractNo,\"splitTractUse\":splitTractUse,\"HDtractList\":HDtractList} ) \n",
    "\n",
    "#outname = STATE+str(int(nTracts))+\"HD2Bnopatch\"+str(int(nDistricts))+\"nW4.csv\"  #solveWedgeB\n",
    "outname = STATE+str(int(nHDs))+\"convexHD2_\"+str(int(nDistricts))+\"nW4_21Mar.csv\"  #solveWedgeC\n",
    "#outname = STATE+str(int(nTracts))+\"HD2Buutpatch\"+str(int(nDistricts))+\"nW4.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "HDdf.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b7a38aff-e4c4-498a-9f63-b6d5d02ab158",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999999999999999\n"
     ]
    }
   ],
   "source": [
    "print(np.average(HDweight) * nHDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "253554da-8842-470e-b9b3-5626823059b0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's ensure normalization of each cluster's usage based on how much of its area must be captured to consider a cluster captured\n",
      "working on cluster no 0 containing counties [43]\n",
      "halfway there! last use was 0.62785\n",
      "our final fractional capture area to normalize this cluster's usage is 0.26146\n",
      "working on cluster no 1 containing counties [44]\n",
      "halfway there! last use was 0.79483\n",
      "our final fractional capture area to normalize this cluster's usage is 0.22671\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#skip this block, use one below if I already CREATED HDVTDLIST'S FROM POLYGONS and there are non-vtd units but all tracts are vtds\n",
    "print(\"Let's ensure normalization of each cluster's usage based on how much of its area must be captured to consider a cluster captured\")\n",
    "#origHDlist = [list() for t in range(nHDs)],\n",
    "splitTractNo, splitTractUse = [-999]*nHDs, [0.]*nHDs\n",
    "origHDpop = [0. for t in range(nHDs)]  #these will be based on captured Census vtd's\n",
    "HDpoly, HDdiam = [dummyPoly for t in range(nHDs)], [0. for t in range(nHDs)]\n",
    "\n",
    "popHDlist = list()  #rebuild here again with the geometries\n",
    "for t in range(nHDs):\n",
    "    if HDweight[t] > 0.000001 :  #HD poly was actually drawn  #[(HDradius[t][w]+0.001*maxD) for w in range(4)]\n",
    "        HDpoly[t] = buildPoly(hdCP[t], HDradius[t], HDangle[t])  #expand to catch the units on HD edge\n",
    "        HDdiam[t] = HDpoly[t].area **0.5  #approximate, for later scoring purposes of underuse * distance\n",
    "        popHDlist.append(t)\n",
    "\n",
    "\n",
    "CCBuse, CCBareaFrac, CCBarea = [0. for CCB in CCBlist], [0.5 for CCB in CCBlist], [geo.area for geo in CCBgeom]\n",
    "for j,geo in enumerate(CCBgeom):\n",
    "    print(\"working on cluster no\",j,\"containing counties\",CCBlist[j] )\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    HDfrac = [ 0. for t in range(nHDs) ]\n",
    "    for t in range(nHDs):\n",
    "        if HDcountyNo[t] in CCBlist[j]:\n",
    "            HDfrac[t] = 1.\n",
    "        else:\n",
    "            HDfrac[t] = HDpoly[t].intersection(geo).area / CCBarea[j]\n",
    "    idx = np.argsort(HDfrac)\n",
    "    i = nHDs\n",
    "    while CCBuse[j] < 1.000 - 0.5 * nDistricts*np.average(HDweight) and i > 0:\n",
    "        i -=1\n",
    "        t = idx[i]\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        if CCBuse[j] > 0.5 and CCBuse[j] - HDweight[t] * nDistricts < 0.5:\n",
    "            print(\"halfway there! last use was\",r5(HDfrac[t]))\n",
    "            plotPoly(HDpoly[t],0.2)\n",
    "        #origHDlist[t].append(unitNo)  #postpone this until main loop\n",
    "        #origHDpop[t] += CCBpop[j]     #postpone until main\n",
    "    if i <= 1:\n",
    "        print(\"WARNING, only\",r5(CCBuse[j]),\" of a district's worth of ensemble intersected the cluster geometry\")\n",
    "        # Add more stuff here to inflate the poly and try again ...\n",
    "    else:\n",
    "        CCBareaFrac[j] = HDfrac[t]\n",
    "        print(\"our final fractional capture area to normalize this cluster's usage is\",r5(CCBareaFrac[j]) )\n",
    "        plotPoly(MAP,0.2)\n",
    "        plotPoly(geo,0.8)\n",
    "        plotCenter(j,geo)\n",
    "        plotPoly(HDpoly[t],1.3)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "868ec654-07ad-4925-bea5-105e8fe9bc59",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDtractList = [list(set(oldHDtractList[t] ) ) for t in range(nHDs)] #restart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a6f48a4-26fe-46a0-84e0-61cf3e042780",
   "metadata": {},
   "outputs": [],
   "source": [
    "#3/10/24 - Georgia -- not sure why unitUse is off here when I pull from original HDtractList.  Used polygons instead, but still off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "63418340-35f5-4a32-a0aa-6b04478b3f57",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will now update vtd lists to either fully take or shed CCBs\n",
      "working on cluster no 1 containing counties [22, 145, 40, 26]\n",
      "halfway there! last use was 0.9714\n",
      "our final fractional capture pop to normalize this cluster's usage is 0.17306 from 202 intersecting HDs\n",
      "Now I will update vtd lists to move full CCB 0 in or out\n",
      "working on cluster no 2 containing counties [118, 67, 138, 126]\n",
      "halfway there! last use was 0.98332\n",
      "our final fractional capture pop to normalize this cluster's usage is 0.3148 from 203 intersecting HDs\n",
      "Now I will update vtd lists to move full CCB 1 in or out\n",
      "working on cluster no 3 containing counties [24]\n",
      "halfway there! last use was 0.95399\n",
      "our final fractional capture pop to normalize this cluster's usage is 0.30829 from 238 intersecting HDs\n",
      "Now I will update vtd lists to move full CCB 2 in or out\n"
     ]
    }
   ],
   "source": [
    "#new for FL - alt to above, using pop-capture threshold on vtd Lists, not area threshold on HDpoly's for including CCBs\n",
    "oldHDtractList = [HDtractList[t].copy() for t in range(nHDs)] #safekeeping\n",
    "print(\"we will now update vtd lists to either fully take or shed CCBs\")\n",
    "CCBuse, CCBpopFracThreshold = [0. for CCB in CCBlist], [0.0001 for CCB in CCBlist]\n",
    "for j,CCBl in enumerate(CCBlist):\n",
    "    CCBtL = list()  #list of tracts in the CCB\n",
    "    for c in CCBl:\n",
    "        CCBtL += countyTractList[c]\n",
    "    addList, nIntersections = list(), 0\n",
    "    print(\"working on cluster no\",j+1,\"containing counties\",CCBl )\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    HDfrac = [ 0. for t in range(nHDs) ]\n",
    "    for t in popHDlist:\n",
    "        if HDcountyNo[t] in CCBl:\n",
    "            HDfrac[t] = 1.\n",
    "        else:\n",
    "            cPop = 0\n",
    "            for v in CCBtL:\n",
    "                if v in oldHDtractList[t]:\n",
    "                    cPop += tractPop[v]\n",
    "            HDfrac[t] = cPop / CCBpop[j]\n",
    "    idx = np.argsort(HDfrac)\n",
    "    i = nHDs\n",
    "    while CCBuse[j] < 1.000 - 0.5 * nDistricts*np.average(HDweight) and i > 0:  #0.5 ... is to land near avg AFTER last added\n",
    "        i -=1\n",
    "        nIntersections +=1\n",
    "        t = idx[i]\n",
    "        addList.append(t)\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        if CCBuse[j] > 0.5 and CCBuse[j] - HDweight[t] * nDistricts < 0.5:\n",
    "            print(\"halfway there! last use was\",r5(HDfrac[t]))\n",
    "    if i <= 1:\n",
    "        print(\"WARNING, only\",r5(CCBuse[j]),\" of a district's worth of ensemble intersected the cluster geometry\")\n",
    "        # Add more stuff here to inflate the poly and try again ...\n",
    "    else:\n",
    "        CCBpopFracThreshold[j] = HDfrac[t]\n",
    "        print(\"our final fractional capture pop to normalize this cluster's usage is\",r5(CCBpopFracThreshold[j]),\n",
    "              \"from\",nIntersections,\"intersecting HDs\" )\n",
    "    print(\"Now I will update vtd lists to move full CCB\",j,\"in or out\")\n",
    "    for t in popHDlist:\n",
    "        if HDfrac[t] > 0:\n",
    "            HDtractList[t] = list(set(HDtractList[t]).difference(CCBtL))\n",
    "            if t in addList:\n",
    "                HDtractList[t] += CCBtL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e2b458c5-1240-49bc-9c8c-752d3604eaff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2157\n"
     ]
    }
   ],
   "source": [
    "print(len(origHDlist))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "beb8908f-1855-4224-9cbe-6f484a193f5a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDunitList = [origHDlist[t].copy() for t in range(nHDs) ]  #for safekeeping - is this from option 1??\n",
    "HDvPop = origHDpop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61c1f835-ecad-444a-9f0e-0ab5c0e394e2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4328e35e-089d-4f81-9987-a2cf0785a4ff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not sure why ./2024state_HD_output/FL7211convexHD2_26nW4.csv isn't giving good polygon-based pops\n",
      "Lets just pull in the HDtractLists from that HD2 code and use them\n",
      "Must have already established unitGeoms, pops, topology\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched vtdlist, e.g. ./2024state_HD_output/GA2698convexHD2_14nW4_10Mar.csv ./2024state_HD_output/GA2698convexHD2_14nW4_10Mar.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I read in 2698 HD lists of vtds\n",
      "working on converting HD 0 from vtd list to unit list\n",
      "working on converting HD 500 from vtd list to unit list\n",
      "working on converting HD 1000 from vtd list to unit list\n",
      "working on converting HD 1500 from vtd list to unit list\n",
      "working on converting HD 2000 from vtd list to unit list\n",
      "working on converting HD 2500 from vtd list to unit list\n",
      "orig unit avg and sd usage are 0.94095 0.25629\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now re-calculate HD pops based on unit assignments\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to correcting for discontiguity and squaring up cluster-unit usage...\n",
      "CCB cluster 0 with pop 176742.0 originally had use of 0.6927301840156793\n",
      "CCB cluster 1 with pop 102191.0 originally had use of 0.9326155527100091\n",
      "CCB cluster 2 with pop 295291.0 originally had use of 0.7581629715265193\n"
     ]
    }
   ],
   "source": [
    "#ALTERNATE RESTART FROM A DECENT HDtractList - this may not be robust for states with county clusters\n",
    "print(\"Not sure why ./2024state_HD_output/FL7211convexHD2_26nW4.csv isn't giving good polygon-based pops\")\n",
    "print(\"Lets just pull in the HDtractLists from that HD2 code and use them\")\n",
    "\n",
    "print(\"Must have already established unitGeoms, pops, topology\")\n",
    "infile = input(\"enter the unpatched vtdlist, e.g. ./2024state_HD_output/GA2698convexHD2_14nW4_10Mar.csv\")\n",
    "inDF = pd.read_csv(infile)\n",
    "vtdListString = inDF[\"HDtractList\"] #HDvtdList\n",
    "nHDs = len(vtdListString)\n",
    "inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "HDweight = inDF[\"HDweight\"]\n",
    "# HDvPop = inDF[\"HDvPop\"].to_list()  #don't care; will rebuild from units\n",
    "hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "print(\"I read in\",nHDs,\"HD lists of vtds\")\n",
    "HDunitList = [list() for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if t%500 == 0:\n",
    "        print(\"working on converting HD\",t,\"from vtd list to unit list\")\n",
    "    for v in inVTDlist[t]:  #this will skip over surrounded units, whose pops we have added to their surrounders\n",
    "        if v in allUnits:\n",
    "            HDunitList[t].append(allUnits.index(v))\n",
    "    for c in unitCounties:\n",
    "        if countyTractList[c][0] in inVTDlist[t]:\n",
    "            u = allUnits.index(c+0.5)\n",
    "            HDunitList[t].append(u)\n",
    "    for j, L in enumerate(CCBlist):\n",
    "        c = L[0]\n",
    "        if countyTractList[c][0] in inVTDlist[t]:\n",
    "            u = allUnits.index(j+0.25)\n",
    "            HDunitList[t].append(u) \n",
    "            \n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD\n",
    "\n",
    "print(\"Now re-calculate HD pops based on unit assignments\")\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "plt.hist([HDvPop[t] for t in popHDlist], bins=50)\n",
    "plt.show()        \n",
    "\n",
    "print(\"prior to correcting for discontiguity and squaring up cluster-unit usage...\")\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"with pop\",unitPop[u],\"originally had use of\",unitUse[u])\n",
    "\n",
    "origHDlist   = [HDunitList[t].copy() for t in range(nHDs) ]  #for safekeeping\n",
    "origHDpop =     HDvPop.copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "22916ca4-1217-4231-9f11-88110a1a7cda",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to correcting for discontiguity and squaring up cluster-unit usage...\n",
      "CCB cluster 0 with pop 263851.0 originally had use of 1.0575578630585878\n"
     ]
    }
   ],
   "source": [
    "print(\"prior to correcting for discontiguity and squaring up cluster-unit usage...\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"with pop\",unitPop[u],\"originally had use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b99795f-d030-456d-9201-6519b739b472",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dc26d08c-a5d9-4e7e-9da5-ae98cf658a3a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If we just ran option 2 with no tracts bigger than vtds, run this but with initial ten rows commented out\n",
      "this optional RESTART block pulls in an existing vtdlist for patching\n",
      "Must have already established unitGeoms, pops, topology\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched vtdlist, e.g. ./2024state_HD_output/CO3108contigUnpatchedB.csv ./2024state_HD_output/FL7211needsEnclaveRemoval.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I read in 7211 HD lists of vtds\n",
      "working on converting HD 0 from vtd list to unit list\n",
      "working on converting HD 500 from vtd list to unit list\n",
      "working on converting HD 1000 from vtd list to unit list\n",
      "working on converting HD 1500 from vtd list to unit list\n",
      "working on converting HD 2000 from vtd list to unit list\n",
      "working on converting HD 2500 from vtd list to unit list\n",
      "working on converting HD 3000 from vtd list to unit list\n",
      "working on converting HD 3500 from vtd list to unit list\n",
      "working on converting HD 4000 from vtd list to unit list\n",
      "working on converting HD 4500 from vtd list to unit list\n",
      "working on converting HD 5000 from vtd list to unit list\n",
      "working on converting HD 5500 from vtd list to unit list\n",
      "working on converting HD 6000 from vtd list to unit list\n",
      "working on converting HD 6500 from vtd list to unit list\n",
      "working on converting HD 7000 from vtd list to unit list\n",
      "orig unit avg and sd usage are 1.00435 0.16718\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"If we just ran option 2 with no tracts bigger than vtds, run this but with initial ten rows commented out\")\n",
    "print(\"this optional RESTART block pulls in an existing vtdlist for patching\")\n",
    "print(\"Must have already established unitGeoms, pops, topology\")\n",
    "\n",
    "#infile = input(\"enter the unpatched vtdlist, e.g. ./2024state_HD_output/CO3108contigUnpatchedB.csv\")\n",
    "#inDF = pd.read_csv(infile)\n",
    "#vtdListString = inDF[\"HDvtdList\"]\n",
    "#nHDs = len(vtdListString)\n",
    "#inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "#HDweight = inDF[\"HDweight\"]\n",
    "#HDvPop = inDF[\"HDvPop\"].to_list()\n",
    "#hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "#hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "#print(\"I read in\",nHDs,\"HD lists of vtds\")\n",
    "\n",
    "HDunitList = [list() for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if t%500 == 0:\n",
    "        print(\"working on converting HD\",t,\"from vtd list to unit list\")\n",
    "    for v in inVTDlist[t]:  #this will skip over surrounded units, whose pops we have added to their surrounders\n",
    "        if v in allUnits:\n",
    "            HDunitList[t].append(allUnits.index(v))\n",
    "    for c in unitCounties:\n",
    "        if countyTractList[c][0] in inVTDlist[t]:\n",
    "            u = allUnits.index(c+0.5)\n",
    "            HDunitList[t].append(u)\n",
    "    for j, L in enumerate(CCBlist):\n",
    "        c = L[0]\n",
    "        if countyTractList[c][0] in inVTDlist[t]:\n",
    "            u = allUnits.index(j+0.25)\n",
    "            HDunitList[t].append(u) \n",
    "            \n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ce9292b0-7d6a-419a-9491-d0def779ad04",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are a total of 2151 populated HDs\n"
     ]
    }
   ],
   "source": [
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if len(HDunitList[t]) > 0:\n",
    "        popHDlist.append(t)\n",
    "populatedTractList = popHDlist.copy()\n",
    "print(\"there are a total of\",len(populatedTractList),\"populated HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f7f36559-7f1d-4203-8b24-0e8cc2ce1753",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this will show if we had any duplicated units in HDunitLists\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"this will show if we had any duplicated units in HDunitLists\")\n",
    "excess = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    excess[t] = len(HDunitList[t]) - len(set(HDunitList[t]))\n",
    "plt.hist(excess)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "27bc7f0a-d7ff-4c68-830f-29ba5013c314",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#popHDlist = populatedTractList.copy()  #somehow our popHDlist started to include the cut District HD starters\n",
    "HDunitList = [list(set(HDunitList[t])) for t in range(nHDs)]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs)]\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "af8a4945-40c2-40df-8dd4-d516f4de2f0f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 702 time is now 10\n",
      "working on HD 1405 time is now 19\n",
      "working on HD 2106 time is now 33\n",
      "out of 2151 total HDs, there were 1634.0 contiguous and 1795.0 complement-contiguous HDs\n",
      "39 HDs had both discontiguity problems, while enclave-only = 317 and discontig only= 478\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 517 317\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"FIND DISCO\" CODE - continue here for option 1 or option 2 drawing method\n",
    "# 3/4/24 - for FL, using 6 or unspecified loops now yields same results\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,6) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53ab9f20-ddb8-47b1-9e5d-5cfda17b2b25",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "40f06e44-45be-4a36-b62c-26698af70e06",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before addressing enclaves, let's triage the discontinuous HDs\n",
      "Now work on dropping smallish disconnected pieces that would keep us above 742387 district pop vs 781460 target\n",
      "we'll also trim any HD with total islands' pop <= 15629\n",
      "working on HD 2\n",
      "working on HD 966\n",
      "working on HD 1393\n",
      "Out of 517 discontig HDs 517 had discontig HDs.\n",
      "Of these, 211 won't be under 742387 after all minor discontigys were shed, while 306 would be too underpopped if we trimmed the discontig pieces\n",
      "here is adjusted pop by original pop for those we trimmed and those we didn't (x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, reclassify after the trimming\n",
      "reclassifying HD 2\n",
      "reclassifying HD 2033\n",
      "There are 306 HDs still with both discontinuity problems\n",
      "Additionally, 15 of the originally discontig HDs are now contig but still have an enclave\n"
     ]
    }
   ],
   "source": [
    "print(\"Before addressing enclaves, let's triage the discontinuous HDs\")\n",
    "#this is the CANTRIM code####\n",
    "\n",
    "#for t in sPgenerator:\n",
    "#    isContig, smallP = isContiguous(filledHDvtdList[t],unitNbrs)\n",
    "#    if isContig:\n",
    "#        print(\"oops!\",t)\n",
    "maxNudgeDownPop = int(0.02 * aDP)\n",
    "minPostFixPop = int(0.95 * aDP)\n",
    "print(\"Now work on dropping smallish disconnected pieces that would keep us above\",minPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "print(\"we'll also trim any HD with total islands' pop <=\",maxNudgeDownPop)\n",
    "nOrigIslands = [0 for t in range(nHDs)]\n",
    "totalIslandPop = [0. for t in range(nHDs)]\n",
    "tryToTrim, canTrim, cantTrim = list(), list(), list()\n",
    "for jj, t in enumerate(sPgenerator):\n",
    "    if jj%200 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    currList, shedList, shedPop = HDunitList[t].copy(), list(),  0.\n",
    "    done,newList = isContiguous(currList,unitNbrs)\n",
    "    if not done:\n",
    "        tryToTrim.append(t)\n",
    "        while not done:\n",
    "            shedList += newList\n",
    "            shedPop += np.sum( [unitPop[u] for u in newList] )\n",
    "            currList = list (  set(currList).difference(set(newList ))  )\n",
    "            done, newList = isContiguous(currList, unitNbrs)\n",
    "            nOrigIslands[t] +=1\n",
    "            \n",
    "        totalIslandPop[t] = shedPop            \n",
    "        if shedPop <= maxNudgeDownPop or HDvPop[t] - shedPop >= minPostFixPop:\n",
    "            canTrim.append(t)\n",
    "            HDvPop[t] -= shedPop\n",
    "            HDunitList[t] = list (set(HDunitList[t]).difference(set(shedList)) )\n",
    "        else:\n",
    "            cantTrim.append(t)\n",
    "print(\"Out of\",len(sPgenerator),\"discontig HDs\",len(tryToTrim),\"had discontig HDs.\")\n",
    "print(\"Of these,\",len(canTrim),\"won't be under\",minPostFixPop,\"after all minor discontigys were shed, while\",\n",
    "      len(cantTrim),\"would be too underpopped if we trimmed the discontig pieces\")\n",
    "plt.scatter([HDvPop[t] + totalIslandPop[t] for t in canTrim],[HDvPop[t] for t in canTrim])\n",
    "plt.scatter([HDvPop[t]                     for t in cantTrim],[HDvPop[t] for t in cantTrim],marker=\"x\")\n",
    "print(\"here is adjusted pop by original pop for those we trimmed and those we didn't (x)\")\n",
    "plt.plot([0.9*aDP, 1.1*aDP],[aDP, aDP], ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, reclassify after the trimming\")\n",
    "badDiscoList, stillHasEnclave = list(), list()\n",
    "for i, t in enumerate(sPgenerator):    \n",
    "    if i%500 == 0:\n",
    "        print(\"reclassifying HD\",t)\n",
    "    unbroken, noEnclave, __, ____ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if not unbroken:\n",
    "        badDiscoList.append(t)  #will do a major triage on these later\n",
    "    if unbroken and not noEnclave:\n",
    "        stillHasEnclave.append(t)\n",
    "print(\"There are\",len(badDiscoList),\"HDs still with both discontinuity problems\")\n",
    "print(\"Additionally,\",len(stillHasEnclave),\"of the originally discontig HDs are now contig but still have an enclave\")\n",
    "eLgenerator += stillHasEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4037f8cb-661b-4032-bfb9-e147975e3bbb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/13/24 - classify further: ID those with adjoiners intersecting the map boundary vs internal islands\n",
      "working on enclave-generating HD 1\n",
      "Out of 332 HDpolys that generated enclaves, 0 had contiguous adjrs, while 330 neighbored a state boundary while 2 had only internal enclaves\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to print the total enclave pops for all enclavy HDs.  This takes a few min; not required 0\n"
     ]
    }
   ],
   "source": [
    "print(\"1/13/24 - classify further: ID those with adjoiners intersecting the map boundary vs internal islands\")\n",
    "internals, edgers, nOK = list(), list(), 0\n",
    "for i,t in enumerate(eLgenerator):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on enclave-generating HD\",t)\n",
    "    twoNbrs = get2nbrs(HDunitList[t],unitNbrs)\n",
    "    isContig, adjSublist = isContiguous(twoNbrs,unitNbrs)\n",
    "    if isContig:\n",
    "        nOK +=1\n",
    "    else:\n",
    "        if len (set(getAdjoiners(HDunitList[t],unitNbrs)).intersection(set(borderUnits)))  > 0:\n",
    "            edgers.append(t)\n",
    "        else:\n",
    "            internals.append(t)\n",
    "print(\"Out of\",len(eLgenerator),\"HDpolys that generated enclaves,\",nOK,\"had contiguous adjrs, while\",len(edgers),\n",
    "      \"neighbored a state boundary while\",len(internals),\"had only internal enclaves\")\n",
    " \n",
    "plotEnclaves = input(\"enter 1 to print the total enclave pops for all enclavy HDs.  This takes a few min; not required\")\n",
    "if str(plotEnclaves) == str(1):\n",
    "    for i,t in enumerate(internals): \n",
    "        if i%500 == 0:\n",
    "            print(\"computing full enclave lists for HD\",t)\n",
    "        fullEnclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        tEpop = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in fullEnclaveLists ] ) \n",
    "        plt.scatter(HDvPop[t],tEpop)\n",
    "    plt.plot([aDP,aDP],[0,5000],ls=\"--\")\n",
    "    print(\"Here are the total enclave pops for enclaving HDs not touching the state boundary vs. the original HD pop\")\n",
    "    plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "05f929ce-4f94-47a6-baf4-60c5a89e2ae5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's fill in all enclaves that won't put us over 820533 district pop vs 781460 target\n",
      "working on enclave-y HD 1254 . We have evaluated 0 of 332 enclavy HDs.Time is now 0\n",
      "working on enclave-y HD 579 . We have evaluated 100 of 332 enclavy HDs.Time is now 3\n",
      "working on enclave-y HD 1490 . We have evaluated 200 of 332 enclavy HDs.Time is now 8\n",
      "working on enclave-y HD 2087 . We have evaluated 300 of 332 enclavy HDs.Time is now 14\n",
      "Out of 332 HDs with enclaves 332 had enclaves.\n",
      "Of these, 155 wouldn't be over 820533 if all enclaves filled, while 177 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"CANFILL\" CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.05 * aDP)\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",maxPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "        \n",
    "for iii, t in enumerate(internals + edgers):\n",
    "    if iii%100 == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(internals+edgers),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Out of\",len(internals+edgers),\"HDs with enclaves\",len(tryToFill),\"had enclaves.\")\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "8987ad1f-d372-415d-a2ec-8fc6736c5102",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 330 39 15\n",
      "177 177 155\n"
     ]
    }
   ],
   "source": [
    "print(len(internals),len(edgers),len(doubleTroubleList),len(set(edgers).intersection(set(doubleTroubleList))))\n",
    "print(len(set(edgers).difference(set(canFill))) ,len(cantFill) , len(canFill))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "8fa887be-92e6-422a-8e80-e00fd3265aab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 0.93628 0.11032\n",
      "CCB cluster 0 = unit 1858 with pop 263851.0 now has use of 0.9824411867166143\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e9d50ef8-39b2-4e22-a11e-cabeda78721e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b759b3c7-b8a3-4d2c-a8fe-50fa93b3e940",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping\n",
    "HDunitList = [savedHDunitList[t].copy() for t in range(nHDs) ]   #restart\n",
    "HDvPop = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7729308-3cd8-4c12-9199-f400f59e6f2e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "551454fb-d57c-466f-8b31-39ccb2a728de",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are the HD centers for 177 cantFill HDs with border or big enclaves\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here are the HD centers for\",len(cantFill),\"cantFill HDs with border or big enclaves\")\n",
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "for t in cantFill:\n",
    "    plotPoly(hdCP[t].buffer(0.1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "5269a224-4a97-417c-99af-6a98df0087ae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 0\n",
      "working on avg unit-to-HDcp dist for HD 802\n",
      "working on avg unit-to-HDcp dist for HD 1606\n"
     ]
    }
   ],
   "source": [
    "barredJettisonSet = set([1858] )  #lock in the clusters  #update this for each state\n",
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce01cce3-e408-4cb1-8761-0cc0be4a57c4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "6e2e44bd-a084-42f6-b533-5dc6d21912a9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this code will solve large enclaves so HD and complement are contiguous for 177 HDs\n",
      "working on HD 17 cantFill 0 of 177 .Time is now 0\n",
      "working on HD 288 cantFill 20 of 177 .Time is now 0\n",
      "working on HD 415 cantFill 40 of 177 .Time is now 1\n",
      "working on HD 652 cantFill 60 of 177 .Time is now 1\n",
      "working on HD 813 cantFill 80 of 177 .Time is now 1\n",
      "working on HD 850 cantFill 100 of 177 .Time is now 2\n",
      "working on HD 1927 cantFill 120 of 177 .Time is now 3\n",
      "working on HD 2026 cantFill 140 of 177 .Time is now 5\n",
      "working on HD 2095 cantFill 160 of 177 .Time is now 6\n",
      "after the MustFree code run, we have the following for cluster usage\n",
      "current avg use and its sd are 0.93463 0.10947\n",
      "CCB cluster 0 = unit 1858 with pop 263851.0 now has use of 0.9824411867166143\n"
     ]
    },
    {
     "data": {
      "image/png": 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pdu7c6ZqzdetWrVixQk888YQOHTqkiooKVVVVqaioyPfOAADAsOH1tzWvW7dO2dnZysnJkXTxyMg777yj8vJylZSUeMzfuHGjJk+erNLSUklScnKy9u/fr7Vr1+ree++VJNXV1WnmzJlauHChJGnKlCl64IEH9NFHH/naFwAAGEa8OsLS09Oj+vp6ZWZmuo1nZmZq3759/e5TV1fnMX/OnDnav3+/ent7JUnf+ta3VF9f7woon332maqrq/Xd7373srV0d3ers7PTbQMAAMOTV0dY2tra1NfXp7i4OLfxuLg4tbS09LtPS0tLv/PPnz+vtrY2TZgwQT/4wQ/05Zdf6lvf+pYsy9L58+f14x//WCtWrLhsLSUlJVq9erU35QMAgCHKp4tug4KC3F5bluUx9lXz/3z8/fff19NPP62ysjJ9/PHHeu211/Sb3/xGTz311GXXLCoqUkdHh2s7ceKEL60AAIAhwKsjLDExMQoODvY4mtLa2upxFOWS8ePH9zs/JCREY8eOlSStWrVKixYtcl0X8/Wvf11dXV36+7//exUXF2vUKM9cFRYWprCwMG/KBwAAQ5RXR1hsNpscDodqamrcxmtqapSent7vPmlpaR7zd+3apdTUVIWGhkqSzp496xFKgoODZVmW62gMAAAYubw+JVRYWKiXX35ZlZWVOnTokAoKCtTU1KTc3FxJF0/VLF682DU/NzdXx48fV2FhoQ4dOqTKykpVVFRo+fLlrjnz589XeXm5duzYoaNHj6qmpkarVq3S97//fQUHB/uhTQAAMJR5fVtzVlaW2tvbtWbNGjmdTqWkpKi6ulqJiYmSJKfT6fZMlqSkJFVXV6ugoEAbNmzQxIkTtX79etctzZK0cuVKBQUFaeXKlWpublZsbKzmz5+vp59+2g8tAgCAoS7IGibnXDo7OxUZGamOjg5FREQMdjkwyB+aO/S9X+zVb/7pW0qJjxzscmAQ/tkABt/Vfn7zXUIAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxvMpsJSVlSkpKUnh4eFyOBzas2fPFefX1tbK4XAoPDxcU6dO1caNGz3mnDp1SkuWLNGECRMUHh6u5ORkVVdX+1IeAHjlSOsZ/aG5w+9b86lzg90aMGyEeLtDVVWV8vPzVVZWppkzZ+qll17S3LlzdfDgQU2ePNlj/tGjRzVv3jw98sgj+uUvf6kPP/xQeXl5io2N1b333itJ6unp0V133aVx48bp1Vdf1aRJk3TixAldf/31194hAFxG9Gib7KHByq9qDMj69tBg7X50luKj7AFZHxhJvA4s69atU3Z2tnJyciRJpaWleuedd1ReXq6SkhKP+Rs3btTkyZNVWloqSUpOTtb+/fu1du1aV2CprKzUn/70J+3bt0+hoaGSpMTERF97AoCrEh9l1+5HZ+lkV4/f1z7Sekb5VY062dVDYAH8wKvA0tPTo/r6eq1YscJtPDMzU/v27et3n7q6OmVmZrqNzZkzRxUVFert7VVoaKjefPNNpaWlacmSJXrjjTcUGxurhQsX6rHHHlNwcLCXLQHA1YuPshMogCHAq8DS1tamvr4+xcXFuY3HxcWppaWl331aWlr6nX/+/Hm1tbVpwoQJ+uyzz/Sf//mfevDBB1VdXa0//vGPWrJkic6fP6+f/vSn/a7b3d2t7u5u1+vOzk5vWgEAAEOITxfdBgUFub22LMtj7Kvm//n4hQsXNG7cOG3atEkOh0M/+MEPVFxcrPLy8suuWVJSosjISNeWkJDgSysAAGAI8CqwxMTEKDg42ONoSmtrq8dRlEvGjx/f7/yQkBCNHTtWkjRhwgR97Wtfczv9k5ycrJaWFvX09H9uuaioSB0dHa7txIkT3rQCAACGEK8Ci81mk8PhUE1Njdt4TU2N0tPT+90nLS3NY/6uXbuUmprqusB25syZOnLkiC5cuOCac/jwYU2YMEE2m63fdcPCwhQREeG2AQCA4cnrU0KFhYV6+eWXVVlZqUOHDqmgoEBNTU3Kzc2VdPHIx+LFi13zc3Nzdfz4cRUWFurQoUOqrKxURUWFli9f7prz4x//WO3t7Vq2bJkOHz6s//iP/9AzzzyjJUuW+KFFAAAw1Hl9W3NWVpba29u1Zs0aOZ1OpaSkqLq62nUbstPpVFNTk2t+UlKSqqurVVBQoA0bNmjixIlav36965ZmSUpISNCuXbtUUFCgW265RfHx8Vq2bJkee+wxP7QIAACGOq8DiyTl5eUpLy+v359t2bLFY2zWrFn6+OOPr7hmWlqa/uu//suXcgAAwDDHdwkBAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDyfvvwQCITmU+d0sqvH7+seaT3j9zUBAAOLwAIjNJ86p9nP1+pcb19A1reHBit6tC0gawMAAo/AAiOc7OrRud4+lWbdphvHjfH7+tGjbYqPsvt9XQDAwCCwwCg3jhujlPjIwS4DAGAYLroFAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA44UMdgEAMJwdaT3j9zWjR9sUH2X3+7qAyQgsABAA0aNtsocGK7+q0e9r20ODtfvRWYQWjCgEFgAIgPgou3Y/Oksnu3r8uu6R1jPKr2rUya4eAgtGFAILAARIfJSdUAH4CRfdAgAA4/kUWMrKypSUlKTw8HA5HA7t2bPnivNra2vlcDgUHh6uqVOnauPGjZedu2PHDgUFBenuu+/2pTQAADAMeR1YqqqqlJ+fr+LiYjU0NCgjI0Nz585VU1NTv/OPHj2qefPmKSMjQw0NDXr88ce1dOlS7dy502Pu8ePHtXz5cmVkZHjfCQAAGLa8Dizr1q1Tdna2cnJylJycrNLSUiUkJKi8vLzf+Rs3btTkyZNVWlqq5ORk5eTk6Ec/+pHWrl3rNq+vr08PPvigVq9eralTp/rWDQAAGJa8Ciw9PT2qr69XZmam23hmZqb27dvX7z51dXUe8+fMmaP9+/ert7fXNbZmzRrFxsYqOzv7qmrp7u5WZ2en2wYAAIYnrwJLW1ub+vr6FBcX5zYeFxenlpaWfvdpaWnpd/758+fV1tYmSfrwww9VUVGhzZs3X3UtJSUlioyMdG0JCQnetAIAAIYQny66DQoKcnttWZbH2FfNvzR++vRpPfTQQ9q8ebNiYmKuuoaioiJ1dHS4thMnTnjRAQAAGEq8eg5LTEyMgoODPY6mtLa2ehxFuWT8+PH9zg8JCdHYsWN14MABHTt2TPPnz3f9/MKFCxeLCwnRJ598omnTpnmsGxYWprCwMG/KBwAAQ5RXgcVms8nhcKimpkb33HOPa7ympkYLFizod5+0tDT9+te/dhvbtWuXUlNTFRoaqunTp+v3v/+9289Xrlyp06dP64UXXuBUj4GaT50LyNM7AQC4HK+fdFtYWKhFixYpNTVVaWlp2rRpk5qampSbmyvp4qma5uZmvfLKK5Kk3NxcvfjiiyosLNQjjzyiuro6VVRUaPv27ZKk8PBwpaSkuL1HVFSUJHmMY/A1nzqn2c/X6lxvn9/XtocGK3q0ze/rAgCGPq8DS1ZWltrb27VmzRo5nU6lpKSourpaiYmJkiSn0+n2TJakpCRVV1eroKBAGzZs0MSJE7V+/Xrde++9/usCA+ZkV4/O9fapNOs23ThujF/X5htoAQCXE2RdugJ2iOvs7FRkZKQ6OjoUEREx2OUMW39o7tD3frFXv/mnbyklPnKwywFGHP4dxHBztZ/ffJcQAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgvJDBLgAA4L0jrWcCsm70aJvio+wBWRu4FgQWABhCokfbZA8NVn5VY0DWt4cGa/ejswgtMA6BBQCGkPgou3Y/Oksnu3r8vvaR1jPKr2rUya4eAguMQ2ABgCEmPspOoMCIw0W3AADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADCeT4GlrKxMSUlJCg8Pl8Ph0J49e644v7a2Vg6HQ+Hh4Zo6dao2btzo9vPNmzcrIyND0dHRio6O1uzZs/XRRx/5UhoAABiGvA4sVVVVys/PV3FxsRoaGpSRkaG5c+eqqamp3/lHjx7VvHnzlJGRoYaGBj3++ONaunSpdu7c6Zrz/vvv64EHHtB7772nuro6TZ48WZmZmWpubva9MwAAMGx4HVjWrVun7Oxs5eTkKDk5WaWlpUpISFB5eXm/8zdu3KjJkyertLRUycnJysnJ0Y9+9COtXbvWNWfr1q3Ky8vTbbfdpunTp2vz5s26cOGC3n33Xd87AwAAw4ZXgaWnp0f19fXKzMx0G8/MzNS+ffv63aeurs5j/pw5c7R//3719vb2u8/Zs2fV29urG2644bK1dHd3q7Oz020DAADDk1eBpa2tTX19fYqLi3Mbj4uLU0tLS7/7tLS09Dv//Pnzamtr63efFStWKD4+XrNnz75sLSUlJYqMjHRtCQkJ3rQCAACGEJ8uug0KCnJ7bVmWx9hXze9vXJKeffZZbd++Xa+99prCw8Mvu2ZRUZE6Ojpc24kTJ7xpAQAADCEh3kyOiYlRcHCwx9GU1tZWj6Mol4wfP77f+SEhIRo7dqzb+Nq1a/XMM89o9+7duuWWW65YS1hYmMLCwrwpHwAADFFeHWGx2WxyOByqqalxG6+pqVF6enq/+6SlpXnM37Vrl1JTUxUaGuoae+655/TUU0/p7bffVmpqqjdlAQCAYc7rU0KFhYV6+eWXVVlZqUOHDqmgoEBNTU3Kzc2VdPFUzeLFi13zc3Nzdfz4cRUWFurQoUOqrKxURUWFli9f7prz7LPPauXKlaqsrNSUKVPU0tKilpYWnTlzxg8tAgCAoc6rU0KSlJWVpfb2dq1Zs0ZOp1MpKSmqrq5WYmKiJMnpdLo9kyUpKUnV1dUqKCjQhg0bNHHiRK1fv1733nuva05ZWZl6enp03333ub3XE088oSeffNLH1gAAwHDhdWCRpLy8POXl5fX7sy1btniMzZo1Sx9//PFl1zt27JgvZQAAgBGC7xICAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHg+ffkhzNd86pxOdvX4fd0jrWf8viYAAF+FwDIMNZ86p9nP1+pcb19A1reHBit6tC0gawMA0B8CyzB0sqtH53r7VJp1m24cN8bv60ePtik+yu73dQGYIRBHUvnvBq4VgWUYu3HcGKXERw52GQCGiOjRNtlDg5Vf1ej3te2hwdr96CxCC3xGYAEASJLio+za/egsv1//dqT1jPKrGnWyq4fAAp8RWAAALvFRdkIFjMRtzQAAwHgEFgAAYDwCCwAAMB6BBQAAGI+LbgEAAyJQT8rmGS8jA4EFABBQgXy+i3TxGS8bFzk0NgBP4CYMmYPAAgAIqEA930WS2rt6lPuv9fph5Ud+X1vigXcmIbAAAAIukM93CVQY4oF3ZiGwAACGNB52NzJwlxAAADAeR1gAALiCQN3dFCjD9UJhAgsAAP0I9N1NgTJcLxQmsAAA0I9A3t0UKMP5QmECyyBrPnUuIF/lDgC4dlzQaw4CyyBqPnVOs5+v1bnePr+vbQ8NVnQAHqIEAMBgILAMopNdPTrX26fSrNt047gxfl17uF50BQAYmQgsBrhx3BilxEcOdhkAABiLwAIAwDATiGsZB/vIPYEFAIBhIpC3Yg/27dIEFgAAholA3Yptwu3SPgWWsrIyPffcc3I6nbr55ptVWlqqjIyMy86vra1VYWGhDhw4oIkTJ+onP/mJcnNz3ebs3LlTq1at0qeffqpp06bp6aef1j333ONLeX4XiFuPJW4/BgD433C9FdvrwFJVVaX8/HyVlZVp5syZeumllzR37lwdPHhQkydP9ph/9OhRzZs3T4888oh++ctf6sMPP1ReXp5iY2N17733SpLq6uqUlZWlp556Svfcc49ef/113X///dq7d6/uvPPOa+/yGgTy1mOJ248BALgaQZZlWd7scOedd+ob3/iGysvLXWPJycm6++67VVJS4jH/scce05tvvqlDhw65xnJzc/W73/1OdXV1kqSsrCx1dnbqrbfecs35m7/5G0VHR2v79u1XVVdnZ6ciIyPV0dGhiIgIb1q6oj80d+h7v9gbkFuPpcG/iAkAgK9y6bPwN//0Lb/f1Xq1n99eHWHp6elRfX29VqxY4TaemZmpffv29btPXV2dMjMz3cbmzJmjiooK9fb2KjQ0VHV1dSooKPCYU1paetlauru71d3d7Xrd0dEh6WLj/nTmdKcudJ/VePsFTb4+yK9rX9Srzs7eAKwLAIB/XPosPHO6U52d/v0svPS5/VXHT7wKLG1tberr61NcXJzbeFxcnFpaWvrdp6Wlpd/558+fV1tbmyZMmHDZOZdbU5JKSkq0evVqj/GEhISrbccraaUBWRYAgCEjkJ+Fp0+fVmTk5Y/e+HTRbVCQe7qyLMtj7Kvm/+W4t2sWFRWpsLDQ9frChQv605/+pNDQUE2ePFknTpzw66mhoaCzs1MJCQkjrnf6pu+RgL5HVt/SyOndsiydPn1aEydOvOI8rwJLTEyMgoODPY58tLa2ehwhuWT8+PH9zg8JCdHYsWOvOOdya0pSWFiYwsLC3MaioqJch5YiIiKG9V/wlYzU3ul7ZKHvkWWk9i2NjN6vdGTlklHeLGiz2eRwOFRTU+M2XlNTo/T09H73SUtL85i/a9cupaamKjQ09IpzLrcmAAAYWbw+JVRYWKhFixYpNTVVaWlp2rRpk5qamlzPVSkqKlJzc7NeeeUVSRfvCHrxxRdVWFioRx55RHV1daqoqHC7+2fZsmX69re/rZ/97GdasGCB3njjDe3evVt79+71U5sAAGAo8zqwZGVlqb29XWvWrJHT6VRKSoqqq6uVmJgoSXI6nWpqanLNT0pKUnV1tQoKCrRhwwZNnDhR69evdz2DRZLS09O1Y8cOrVy5UqtWrdK0adNUVVXl0zNYwsLC9MQTT3icLhoJRmrv9E3fIwF9j6y+pZHde3+8fg4LAADAQPPqGhYAAIDBQGABAADGI7AAAADjEVgAAIDxhmRgKSsrU1JSksLDw+VwOLRnz54rzu/u7lZxcbESExMVFhamadOmqbKycoCq9R9v+n744YcVFBTksd18880DWLH/ePt3vnXrVt1666267rrrNGHCBP3d3/2d2tvbB6ha//G27w0bNig5OVl2u1033XST6/ECQ8kHH3yg+fPna+LEiQoKCtKvfvWrr9yntrZWDodD4eHhmjp1qjZu3Bj4Qv3M276dTqcWLlyom266SaNGjVJ+fv6A1Olv3vb92muv6a677lJsbKwiIiKUlpamd955Z2CK9SNv+967d69mzpypsWPHym63a/r06fr5z38+MMUaYsgFlqqqKuXn56u4uFgNDQ3KyMjQ3Llz3W6l/kv333+/3n33XVVUVOiTTz7R9u3bNX369AGs+tp52/cLL7wgp9Pp2k6cOKEbbrhBf/u3fzvAlV87b3vfu3evFi9erOzsbB04cED//u//rv/+7/9WTk7OAFd+bbztu7y8XEVFRXryySd14MABrV69WkuWLNGvf/3rAa782nR1denWW2/Viy++eFXzjx49qnnz5ikjI0MNDQ16/PHHtXTpUu3cuTPAlfqXt313d3crNjZWxcXFuvXWWwNcXeB42/cHH3ygu+66S9XV1aqvr9d3vvMdzZ8/Xw0NDQGu1L+87Xv06NH6x3/8R33wwQc6dOiQVq5cqZUrV2rTpk0BrtQg1hBzxx13WLm5uW5j06dPt1asWNHv/LfeesuKjIy02tvbB6K8gPG277/0+uuvW0FBQdaxY8cCUV5Aedv7c889Z02dOtVtbP369dakSZMCVmMgeNt3WlqatXz5crexZcuWWTNnzgxYjYEmyXr99devOOcnP/mJNX36dLexf/iHf7C++c1vBrCywLqavv/crFmzrGXLlgWsnoHibd+XzJgxw1q9erX/CxogvvZ9zz33WA899JD/CzLUkDrC0tPTo/r6emVmZrqNZ2Zmat++ff3u8+abbyo1NVXPPvus4uPj9bWvfU3Lly/XuXPnBqJkv/Cl779UUVGh2bNnux7wN1T40nt6ero+//xzVVdXy7Is/e///q9effVVffe73x2Ikv3Cl767u7sVHh7uNma32/XRRx+pt7c3YLUOtrq6Oo/f05w5c7R///5h3TcuunDhgk6fPq0bbrhhsEsZUA0NDdq3b59mzZo12KUMmCEVWNra2tTX1+fxpYhxcXEeX554yWeffaa9e/fqD3/4g15//XWVlpbq1Vdf1ZIlSwaiZL/wpe8/53Q69dZbbw25UyKSb72np6dr69atysrKks1m0/jx4xUVFaVf/OIXA1GyX/jS95w5c/Tyyy+rvr5elmVp//79qqysVG9vr9ra2gai7EHR0tLS7+/p/Pnzw7pvXPT888+rq6tL999//2CXMiAmTZqksLAwpaamasmSJUPyv+u+GlKB5ZKgoCC315ZleYxdcuHCBQUFBWnr1q264447NG/ePK1bt05btmwZUkdZJO/6/nNbtmxRVFSU7r777gBVFnje9H7w4EEtXbpUP/3pT1VfX6+3335bR48edX3f1VDiTd+rVq3S3Llz9c1vflOhoaFasGCBHn74YUlScHBwoEsdVP39nvobx/Cyfft2Pfnkk6qqqtK4ceMGu5wBsWfPHu3fv18bN25UaWmp2/fyDXdDKrDExMQoODjY4/8wW1tbPf4P65IJEyYoPj7e7aurk5OTZVmWPv/884DW6y++9H2JZVmqrKzUokWLZLPZAllmQPjSe0lJiWbOnKl//ud/1i233KI5c+aorKxMlZWVcjqdA1H2NfOlb7vdrsrKSp09e1bHjh1TU1OTpkyZouuvv14xMTEDUfagGD9+fL+/p5CQEI0dO3aQqkKgVVVVKTs7W//2b/+m2bNnD3Y5AyYpKUlf//rX9cgjj6igoEBPPvnkYJc0YIZUYLHZbHI4HKqpqXEbr6mpUXp6er/7zJw5U1988YXOnDnjGjt8+LBGjRqlSZMmBbRef/Gl70tqa2t15MgRZWdnB7LEgPGl97Nnz2rUKPd/tC8dYbCGyFdnXcvfeWhoqCZNmqTg4GDt2LFD3/ve9zx+H8NJWlqax+9p165dSk1NVWho6CBVhUDavn27Hn74YW3btm1IXZvmb5Zlqbu7e7DLGDiDdLGvz3bs2GGFhoZaFRUV1sGDB638/Hxr9OjRrrtfVqxYYS1atMg1//Tp09akSZOs++67zzpw4IBVW1tr/dVf/ZWVk5MzWC34xNu+L3nooYesO++8c6DL9Stve/+Xf/kXKyQkxCorK7M+/fRTa+/evVZqaqp1xx13DFYLPvG2708++cT613/9V+vw4cPWb3/7WysrK8u64YYbrKNHjw5SB745ffq01dDQYDU0NFiSrHXr1lkNDQ3W8ePHLcvy7Puzzz6zrrvuOqugoMA6ePCgVVFRYYWGhlqvvvrqYLXgE2/7tizLNd/hcFgLFy60GhoarAMHDgxG+T7ztu9t27ZZISEh1oYNGyyn0+naTp06NVgt+MTbvl988UXrzTfftA4fPmwdPnzYqqystCIiIqzi4uLBamHADbnAYlmWtWHDBisxMdGy2WzWN77xDau2ttb1sx/+8IfWrFmz3OYfOnTImj17tmW3261JkyZZhYWF1tmzZwe46mvnbd+nTp2y7Ha7tWnTpgGu1P+87X39+vXWjBkzLLvdbk2YMMF68MEHrc8//3yAq7523vR98OBB67bbbrPsdrsVERFhLViwwPqf//mfQaj62rz33nuWJI/thz/8oWVZ/f99v//++9btt99u2Ww2a8qUKVZ5efnAF36NfOm7v/mJiYkDXvu18LbvWbNmXXH+UOFt3+vXr7duvvlm67rrrrMiIiKs22+/3SorK7P6+voGp4FBEGRZQ+QYOQAAGLGG74ltAAAwbBBYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGC8/wc6woH/9Ft12AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THIS IS THE MUSTFREE CODE - for high-pop HDs with map-border enclaves, can't fill; \n",
    "#  must jettison some inHD map-boundary units to connect the enclave to the larger complement\n",
    "#For each HD to be triaged, define the list(s) of contiguous enclave units that are on the map boundary, \n",
    "#  and the in-HD map-boundary segments.  ID which in-HD MBS's adjoin one vs. two enclaves.\n",
    "#     Fill all internal enclaves, and all boundary enclaves < 0.05 aDP.\n",
    "#   Then each loop, look for boundary enclaves that can be filled without overpopping.\n",
    "#     If none, pick the 1/2 has-1-boundary-enclave-neighbor that is least painful to jettison to free an enclave.\n",
    "#       (Jettison score based on pop-to-Free and distance from HDcP)\n",
    "#         Update HDpop, HDlist, and enclave & HD-boundary associations, then re-loop\n",
    "# Later, we will swell-fill to square up pop (w/ checkEnclave) biasing toward close-to-hdCP, underused\n",
    "borderSet = set(borderUnits)\n",
    "debug, debugPlot = False, False\n",
    "startTime = time.time()  #takes about 1.5sec per HD triage\n",
    "clusterJettisonBoost = 3.  #this discourages jettisoning of underused clusters\n",
    "print(\"this code will solve large enclaves so HD and complement are contiguous for\",len(cantFill),\"HDs\")\n",
    "nEnclavesPerHD = [0 for t in range(nHDs)]\n",
    "HDenclaveLists = [list() for t in range(nHDs)]\n",
    "for iii,t in enumerate(cantFill):\n",
    "    if iii %20 == 0:\n",
    "        print(\"working on HD\",t,\"cantFill\",iii,\"of\",len(cantFill),\".Time is now\",int(time.time() - startTime) )\n",
    "    shedUs = list()    \n",
    "    enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "    nEnclavesPerHD[t] = len(enclaveLists)\n",
    "    HDenclaveLists[t] = enclaveLists.copy()\n",
    "    if debug:\n",
    "        print(\"pre-adjust HDpop for HD\",t,\"is\",HDvPop[t],\"I found\",len(enclaveLists),\"TOTAL enclaves\")\n",
    "    internalEnclaves, edgeEnclaves, combinedEnclBorderList = list(), list(), list()\n",
    "    for jjj, eL in enumerate(enclaveLists.copy() ):\n",
    "        enclaveBorderList = list ( set(eL).intersection(borderSet) )\n",
    "        if debug:\n",
    "            print(\"In enclave\",jjj,\"I found\",len(enclaveBorderList),\"units that were enclaved and are in the map borderSet\")\n",
    "        combinedEnclBorderList += enclaveBorderList\n",
    "        ePop = np.sum( [unitPop[u] for u in eL] )\n",
    "        if len(enclaveBorderList) == 0 or ePop < 0.05 * aDP:  #internal or small enclave.  Fill it\n",
    "            if ePop > 0:  #in a prev treatment, could be an empty (surrounded) unit that we don't care about\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += ePop\n",
    "            internalEnclaves.append(eL)\n",
    "        else:\n",
    "            edgeEnclaves.append(eL)\n",
    "    if debug:\n",
    "        print(\"Of these\",len(internalEnclaves),\"were internal or small, so I filled them, leaving\",\n",
    "              len(edgeEnclaves),\"non-small border = edge enclaves\" )\n",
    "    if debugPlot:\n",
    "        print(\"Here is the original HD, internal enclaves ('x') and non-small border enclaves by enclave no\")\n",
    "        plotPoly(hdCP[t].buffer(0.3))\n",
    "        for u in HDunitList[t]:\n",
    "            if unitPop[u] > 0:\n",
    "                plotPoly(unitGeom[u],0.2)\n",
    "        for L in internalEnclaves:\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u],1.5)\n",
    "                    plotCenter(\"x\",unitCP[u],8)\n",
    "        for L in edgeEnclaves: #############\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u])\n",
    "                    #plotCenter(\"e\",unitCP[u],8)\n",
    "        #plotPoly(MAP,0.4)\n",
    "        #plt.show()\n",
    "    enclaveLists, combinedEnclBorderSet = list(), set(combinedEnclBorderList)\n",
    "    if len(edgeEnclaves) > 0:\n",
    "        # Now, we order by chain connectivity of HD and enclave sublists to be H0-E0-H1-E1 ... En-Hn+1\n",
    "        nonHDborderSet = borderSet.difference(  set(HDunitList[t]) )\n",
    "        nonHDlongBorderSet = nonHDborderSet.difference(combinedEnclBorderSet) #long=non HD boundary excluding enclaves\n",
    "        remainingEnclBorderSet = combinedEnclBorderSet.copy()\n",
    "        remainingHDborderSet =   borderSet.intersection(set(HDunitList[t]) )\n",
    "        #Now find the \"HDender\" unit which borders the non-enclave nonHD boundary, then find opp end of this HD bdry piece\n",
    "        HDender = list(set(getAdjoiners(nonHDlongBorderSet,unitNbrs)).intersection(remainingHDborderSet) )[0]\n",
    "        firstHDborderSet = getContigFromStarter(HDender,remainingHDborderSet,unitNbrs)\n",
    "        HDstarter = list(set(getAdjoiners(remainingEnclBorderSet,unitNbrs)).intersection(firstHDborderSet))[0]\n",
    "        HDborderSublists = [ list(firstHDborderSet) ]\n",
    "        unused = [1 for eL in edgeEnclaves]\n",
    "        eLadjoiners = [getAdjoiners(eL,unitNbrs) for eL in edgeEnclaves ]\n",
    "        for j in range(len(edgeEnclaves)): #find the enclave with the most HDstarter neighbors (at least 1)\n",
    "            found = False\n",
    "            for i,eL in enumerate(edgeEnclaves):\n",
    "                if unused[i] == 1:\n",
    "                    HDadjSet = set(HDborderSublists[j]).intersection(set(eLadjoiners[i]))\n",
    "                    if len(HDadjSet) > 0:\n",
    "                        unused[i] = 0\n",
    "                        eNo = i\n",
    "                        found = True\n",
    "                        remainingEnclBorderSet = remainingEnclBorderSet.difference(set(edgeEnclaves[eNo]) )\n",
    "                        enclaveLists.append(edgeEnclaves[eNo])\n",
    "                        remainingHDborderSet = remainingHDborderSet.difference(set(HDborderSublists[j]) )\n",
    "                        HDstarter = list(set(eLadjoiners[i]).intersection(remainingHDborderSet))[0]       \n",
    "                        HDborderSublists.append(getContigFromStarter(HDstarter,remainingHDborderSet,unitNbrs) )\n",
    "                        break\n",
    "                if found:\n",
    "                    break\n",
    "\n",
    "        enclavePops = [np.sum([unitPop[u] for u in L]) for L in enclaveLists]\n",
    "        #print(\"prior to adjustments, border enclavePops are\",enclavePops)         \n",
    "\n",
    "        nHDBS, nEnclaves = len(HDborderSublists), len(enclaveLists)\n",
    "        popToFree, freeingCounties, freeingUnits = [0. for n in range(nHDBS)], [list() for n in range(nHDBS) \n",
    "                                                                               ],[list() for n in range(nHDBS)]\n",
    "        for j,L in enumerate(HDborderSublists):\n",
    "            for u in L:\n",
    "                if int(allUnits[u]) == allUnits[u]:  #this border unit is a vtd\n",
    "                    c = countyNo[parentVTDno[allUnits[u]]]   #countyNo[allUnits[u]]\n",
    "                    if c not in freeingCounties[j]:\n",
    "                        if countyPop[c] < 0.1 * aDP:  #plan to add all in-county pop\n",
    "                            freeingCounties[j].append(c)\n",
    "                        else:\n",
    "                            popToFree[j] += unitPop[u]\n",
    "                            freeingUnits[j].append(u)\n",
    "                else: #border unit is a full county or cluster, or in a dense county.  Add the whole unit pop\n",
    "                    popToFree[j] += unitPop[u]\n",
    "                    freeingUnits[j].append(u)\n",
    "            for c in freeingCounties[j]:  #include ALL in-HD pop from each non-unit border county, not just its border units\n",
    "                #plotPoly(countyGeom[c],0.1)\n",
    "                for u in countyUnitList[c]:\n",
    "                    if u in HDunitList[t]:\n",
    "                        popToFree[j] += unitPop[u]\n",
    "                        freeingUnits[j].append(u)\n",
    "        freeingCP = [ getHDcp(  unitCP, unitPop, L) for L in freeingUnits ]\n",
    "        freeingScore = [ pTF/aDP - 0.5*freeingCP[j].distance(hdCP[t]) / avgDist[t] for j,pTF in enumerate(popToFree) ]\n",
    "        for j, fU in enumerate(freeingUnits):  #in above 0.5 is a fudgy\n",
    "            if len(barredJettisonSet.intersection(set(fU)) ) > 0: #has a cluster we want to hold onto\n",
    "                freeingScore[j] += clusterJettisonBoost #  ..so increase its score (make it harder to drop)\n",
    "        if debugPlot:\n",
    "            print(\"plotting the freeing units with negative numbers for each freeing group\")\n",
    "            for j,L in enumerate(freeingUnits):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        #plotPoly(unitGeom[u],0.5)\n",
    "                        plotCenter(\"-\"+str(j),unitGeom[u],6)\n",
    "                        pass\n",
    "            print(\"plotting the enclaveLists, with + numbers for each enclave\")\n",
    "            for j,L in enumerate(enclaveLists):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        plotPoly(unitGeom[u])\n",
    "                        plotCenter(j,unitCP[u],8)\n",
    "                        pass\n",
    "    \n",
    "    while len(enclaveLists) > 0:\n",
    "        if HDvPop[t] + np.min(enclavePops) < 1.05*aDP or np.min(enclavePops) < 0.05 * aDP:  #fill the smallest enclave\n",
    "            eNo = enclavePops.index(np.min(enclavePops))\n",
    "            HDunitList[t] += enclaveLists[eNo]\n",
    "            HDvPop[t] += enclavePops[eNo]\n",
    "            #print(t,\"=t. Absorbing enclave\",eNo,\"with pop\",enclavePops[eNo],\"HD total pop now\",int(HDfreePop[t]) )\n",
    "            enclaveBUs = list(set(enclaveLists[eNo]).intersection(set(borderUnits)) )\n",
    "            enclaveBpop = np.sum([unitPop[u] for u in enclaveBUs])\n",
    "            sNo, dropped_sNo = eNo, eNo+1\n",
    "            freeingScore[sNo] += freeingScore[sNo+1]+enclaveBpop/aDP\n",
    "            freeingUnits[sNo] += freeingUnits[sNo+1]+enclaveBUs\n",
    "            popToFree[sNo]    +=    popToFree[sNo+1]+enclaveBpop\n",
    "        else: #jettison one of the two end HD border lists; pick the less painful path to freedom\n",
    "            distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "            starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "            eNo, dropped_sNo = 0,0\n",
    "            if starterU not in freeingUnits[-1]:  #we can't drop the home unit, even to save an underused corner\n",
    "                if freeingScore[-1] < freeingScore[0] or starterU in freeingUnits[0]:\n",
    "                    eNo, dropped_sNo = len(enclaveLists)-1,len(enclaveLists)\n",
    "            barredIncluded0 = barredJettisonSet.intersection(set(freeingUnits[0]))\n",
    "            barredIncluded_1 = barredJettisonSet.intersection(set(freeingUnits[-1]))\n",
    "            #if len(barredIncluded0.union(barredIncluded_1)) > 0:\n",
    "            #    print(\"We are considering dropping an end unit to free from t\",t,\". Chosen, beg, end, scores are\")\n",
    "            #    print(dropped_sNo,barredIncluded0, barredIncluded_1, freeingScore[0], freeingScore[-1])\n",
    "            HDunitList[t] = list( set(HDunitList[t]).difference(set(freeingUnits[dropped_sNo]) ) )\n",
    "            HDvPop[t] -= popToFree[dropped_sNo]\n",
    "            #print(t,\"= t. Jettisoning HDborder\",dropped_sNo,\"with popToFree\",popToFree[dropped_sNo] )\n",
    "        del enclaveLists[eNo]\n",
    "        del enclavePops[eNo]\n",
    "        if debug:\n",
    "            print(t,\"= t. Now we have\",len(enclaveLists),\"left trapped.  Picked eNo, freeScores were\", eNo,freeingScore)\n",
    "        del freeingScore[dropped_sNo]\n",
    "        del freeingUnits[dropped_sNo]\n",
    "        del popToFree   [dropped_sNo] \n",
    "\n",
    "    #plotPoly(MAP,0.4)\n",
    "    if debugPlot:\n",
    "        plt.show()    \n",
    "        \n",
    "unitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t]*nDistricts\n",
    "print(\"after the MustFree code run, we have the following for cluster usage\")\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "ca9ce3cb-c150-4f39-ae55-f781d39d7f57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "postMustFreeUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "c32dd108-073b-48aa-99ce-fea50c8d023c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([HDvPop[t] for t in popHDlist] )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "f58fe2d0-3c62-412f-8984-ff5d67ea9b10",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After above work, re-classification of contiguity problems?\n",
      "working on HD 0\n",
      "working on HD 500\n",
      "working on HD 1000\n",
      "working on HD 1500\n",
      "working on HD 2000\n",
      "out of 2151 total HDs, there were 1829 307 0 15 HDs that were clean, discontig only, enclave-only, both problems after triage\n"
     ]
    }
   ],
   "source": [
    "#THIS is the FINDSTILLBROKEN code\n",
    "print(\"After above work, re-classification of contiguity problems?\")\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "for t in popHDlist:\n",
    "    if t%500 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "\n",
    "#40 5 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "74c4c39d-a238-4f10-81c4-949f67ea0d70",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing HDdiam for HD 300\n",
      "computing HDdiam for HD 600\n",
      "computing HDdiam for HD 1200\n",
      "computing HDdiam for HD 1500\n",
      "computing HDdiam for HD 2100\n",
      "computing HDdiam for HD 2400\n",
      "computing HDdiam for HD 2700\n",
      "computing HDdiam for HD 3000\n",
      "computing HDdiam for HD 3300\n",
      "computing HDdiam for HD 3600\n",
      "computing HDdiam for HD 3900\n",
      "computing HDdiam for HD 4200\n",
      "computing HDdiam for HD 4500\n",
      "computing HDdiam for HD 4800\n",
      "computing HDdiam for HD 5100\n",
      "computing HDdiam for HD 5400\n",
      "computing HDdiam for HD 5700\n",
      "computing HDdiam for HD 6000\n",
      "computing HDdiam for HD 6300\n",
      "computing HDdiam for HD 6600\n",
      "computing HDdiam for HD 6900\n"
     ]
    }
   ],
   "source": [
    "#optional HDpoly building  -- this might be slow\n",
    "HDpoly = [hdCP[t] for t in range(nHDs)]\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if t %300 == 0:\n",
    "        print(\"building HD shape from units for HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        HDpoly[t] = HDpoly[t].union(unitGeom[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "1ef683a2-4429-4a91-b1ea-a9b0950dea22",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on current HD diam for HD number 0\n",
      "working on current HD diam for HD number 802\n",
      "working on current HD diam for HD number 1606\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HDdiam = [0. for t in range(nHDs)]\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on current HD diam for HD number\",t)\n",
    "    HDdiam[t] = (HDpoly[t].intersection(MAP) ).area ** 0.5\n",
    "plt.hist([HDdiam[t] for t in popHDlist])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "45e3c17c-8c16-4bc5-b7fc-25b9250ed11a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we will regrow all disco'd HDs off by more than  39073 but less than 195365\n",
      "... from the contiguous block that contains the hdCP (not full regrowth).  We will swell out, avoiding enclaves\n",
      "This is a total of 322 HDs to triage in this block\n",
      "working on swell-filling discontig HD 8 our 1 th HD we think we can swell out of 322\n",
      "working on swell-filling discontig HD 493 our 41 th HD we think we can swell out of 322\n",
      "working on swell-filling discontig HD 729 our 81 th HD we think we can swell out of 322\n",
      "working on swell-filling discontig HD 946 our 121 th HD we think we can swell out of 322\n",
      "working on swell-filling discontig HD 1046 our 161 th HD we think we can swell out of 322\n",
      "working on swell-filling discontig HD 1105 our 201 th HD we think we can swell out of 322\n",
      "working on swell-filling discontig HD 1322 our 241 th HD we think we can swell out of 322\n",
      "working on swell-filling discontig HD 1730 our 281 th HD we think we can swell out of 322\n",
      "working on swell-filling discontig HD 1387 our 321 th HD we think we can swell out of 322\n",
      "we partially regrew 128 HDs, leaving 194 to regrow from scratch\n"
     ]
    }
   ],
   "source": [
    "### THIS IS THE CANSWELL CODE\n",
    "print(\"Here, we will regrow all disco'd HDs off by more than \",int(aDP-minPostFixPop),\"but less than\",int(0.25*aDP) )\n",
    "print(\"... from the contiguous block that contains the hdCP (not full regrowth).  We will swell out, avoiding enclaves\")\n",
    "HDnAddedUnits = [0 for t in range(nHDs)]\n",
    "maxGap = 0.9 * np.median(unitPop)  #set a reasonable gap prior to picking the last block\n",
    "HDdiam = [HDpoly[t].area ** 0.5 for t in range(nHDs) ] #in case not defined before\n",
    "badDiscoList = list()\n",
    "nCanSwell = 0\n",
    "triageList = discontigOnly + bothProblems\n",
    "print(\"This is a total of\",len(triageList),\"HDs to triage in this block\")\n",
    " \n",
    "for i,t in enumerate(triageList): #canSwell\n",
    "    if i%40 == 0:\n",
    "        print(\"working on swell-filling discontig HD\",t,\"our\",i+1,\"th HD we think we can swell out of\",len(triageList) )\n",
    "    distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "    starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = getContigFromStarter(starterU, HDunitList[t], unitNbrs)\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    if contigPop < 0.75 * aDP or contigPop > 2.*aDP - minPostFixPop:  \n",
    "        badDiscoList.append(t)\n",
    "    else: #rebuild from this big piece\n",
    "        nCanSwell +=1\n",
    "        gap = aDP - contigPop\n",
    "        origGap = gap\n",
    "        currList, addedList = contigUs.copy(), list()\n",
    "        adjoiners = getAdjoiners(currList, unitNbrs)\n",
    "        nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "        nearHDscore = [ (unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "            HDpoly[t]) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] for uu in adjoiners ] \n",
    "        stillGoing = True\n",
    "\n",
    "        while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "            while i < len(nearHDscore) and notYetPicked:        \n",
    "                listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "                canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "                if canAdd:\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't add any more units without creating an enclave\n",
    "            else: #we selected the best unit to add legally\n",
    "                gap -= unitPop[unitNoToAdd]\n",
    "                addedList.append(unitNoToAdd)  #so add it to our growing HD ...\n",
    "                currList.append( unitNoToAdd)\n",
    "                for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                    if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append((unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "                            HDpoly[t]) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] )\n",
    "                del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                for i, uu in enumerate(nearHDlist.copy()):\n",
    "                    if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                        del nearHDscore[nearHDlist.index(uu)]\n",
    "                        del nearHDlist[ nearHDlist.index(uu)]\n",
    "        for u in addedList:\n",
    "            unitUse[u] += HDweight[t] * nDistricts\n",
    "        for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "            unitUse[u] -= HDweight[t] * nDistricts       \n",
    "        HDunitList[t] = contigUs + addedList\n",
    "        HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "        HDnAddedUnits[t] = len(addedList)\n",
    "    \n",
    "badDiscoList += enclaveOnly\n",
    "print(\"we partially regrew\",nCanSwell,\"HDs, leaving\",len(badDiscoList),\"to regrow from scratch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f57325dd-523c-466a-80cb-8e10b739b4ac",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "previous avg use and its sd are 0.93463 0.10947\n",
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 0.93377 0.10229\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"previous avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "0ce918e1-0d30-4930-9fb8-0769d793683c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 1004 time is now 4\n",
      "working on HD 2006 time is now 8\n",
      "out of 2151 total HDs, there were 1957.0 contiguous and 2140.0 complement-contiguous HDs\n",
      "3 HDs had both discontiguity problems, while enclave-only = 8 and discontig only= 191\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 194 8\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZGRl6//33NWPGDElSZGSkRo8erddff93rPZmZmbrpppsUFhamiooKXXPNNQoLC9PGjRu1adMmhYWF6ac//WmT/EJ/9NFH9bOf/UyfffaZbr31Vo0bN067d++WdOL36U9/+lO1b99eW7du1Ztvvqn169dr6tSpXtt47733tHv3bm3YsEFvvPGGVq5c2TpOVTA+mDhxounSpYt5++23TX5+vlmxYoXp1KmTmTFjhjVn3rx5JjIy0qxYscLk5eWZcePGmc6dO5uysrKz2kdpaamRZEpLS30p7azkfVVius182+R9VdLo224q/lgzgLNXWVlpdu3aZSorK/8zeHC7MXMiTvxpBx/3d/jwYdO2bVuTk5PjNX7nnXeacePGGWOM2bBhg5Fk1q9fb61fs2aNkWT1OmTIEDNhwoTT7qdbt27mD3/4w2nX/+///q/p2LGj9XrFihUmLCzMHDlyxBhz4vdJ27ZtzZo1a4wxxrz88svm4osvNrW1tdZ7qqqqjMvlMmvXrv3BvufMmWP69+//g/OMMUaSmTRpktfY4MGDzW9+8xtjjDEvvfSSad++vTl8+LC1fs2aNaZNmzamsLDQGHPid26HDh2sfowxZvHixSYsLMwcP378rOpoCqf8mTW+/f726ZyP5557To8++qgmT56soqIixcTE6J577tHvfvc7a86MGTNUWVmpyZMnq7i4WIMHD9a6desUHh7emJkJANBMdu3apaNHj2rkyJFe49XV1RowYIDXWL9+/ay/d+7cWdKJj+IvvPBC5ebm6u677z7r/W7YsEGpqanatWuXysrKVFNTo6NHj+rIkSMKDQ3V6NGjFRgYqNWrV2vs2LF66623FB4ebl2BuW3bNu3du7fe76OjR4/qH//4h09fg7MxZMiQeq/rTlbdvXu3+vfvr9DQUGv9VVddpdraWu3Zs8c6VaF///4KCQnx2sbhw4dVUFCgbt26NXrNdvEpfISHhys9Pd26tPZUHA6HPB6PPB7POZYGAGiJ6j5qX7Nmjbp06eK1zul0er0OCgqy/l534UHd+10u11nvc//+/br++us1adIkPf744+rQoYM2bdqkO++8U8eOHZMkBQcH6+c//7kyMzM1duxYZWZm6pZbblFgYKC134EDB9b7aEaSLrjggrOu5VzUfQ3MGS7EON24r3NaMp7tAgDwSZ8+feR0OnXgwAH17NnTa/HlasV+/frpvffeO6u5n3zyiWpqarRgwQJdeeWV6tWrl77++ut68yZMmKB3331XO3fu1IYNGzRhwgRr3eWXX64vv/xSUVFR9eqOjIw867rP1ubNm+u97t27t6QTX8Pc3FwdOXLEWv/hhx+qTZs26tXrPycAf/bZZ6qsrPTaRlhYmLp27dro9dqJ8AEA8El4eLh++9vf6v7771dGRob+8Y9/aPv27Xr++eeVkZFx1tuZM2eO3njjDc2ZM0e7d+9WXl6e5s+ff8q5F110kWpqavTcc89p3759+vOf/6wXX3yx3rzExERFR0drwoQJ6t69u6688kpr3YQJE9SpUyfddNNN+vvf/678/HxlZ2dr2rRp+uqrr05b5969e5Wbm6vCwkJVVlYqNzdXubm5P3iS6ptvvqk//elP+uKLLzRnzhx9/PHH1gmlEyZMUNu2bTVx4kTt2LFDGzZs0L333qvbbrvN6+rQ6upq3Xnnndq1a5feeecdzZkzR1OnTq138Ye/8eljFwCAjb79osXu5/HHH1dUVJTS0tK0b98+tWvXTpdffrkefvjhs97G8OHD9eabb+rxxx/XvHnzFBERoauvvvqUcy+77DItXLhQTz75pGbNmqWrr75aaWlp+uUvf+k1z+FwaNy4cXrqqae8zkeUpJCQEG3cuFEzZ85UcnKyysvL1aVLF40YMeKMt3m46667vK7iqTuvJT8/X927dz/t++bOnatly5Zp8uTJcrvdev3119WnTx+rlrVr12ratGm64oorFBISop/97GdauHCh1zZGjBih+Ph4XX311aqqqtLYsWNbxWkNDmOMae4iTlZWVqbIyEiVlpY2+j0/dhws1Q3PbdLb9w5TQpfGP8TWFPyxZgBn7+jRo8rPz1dcXJzatm17YpA7nPo9h8OhlStXasyYMQ3exu23366SkhKtWrWq0epqDKf8mZVvv7858gEALU272BNB4Dx/tgtaL8IHALRE7WIJA2i1CB8AADSyxjij4ZVXXjn3Qloo/z5dFgAA+B3CBwAAsBXhAwBagBZ24SFwWo3xs0r4AIBmVHf78YoKGy+rBc5B3c/qybfO9xUnnAJAMwoICFC7du1UVFQk6cTNp/z9uR1onYwxqqioUFFRkdq1a6eAgIAGb4vwAQDNzO12S5IVQICWrF27dtbPbEMRPgCgmTkcDnXu3FlRUVHWE1qBligoKOicjnjUIXwAQAsREBDQKP9jB1o6TjgFAAC2InwAAABbET4AAICtCB8AAMBWhA8AAGArwgcAALAV4QMAANiK8AEAAGxF+AAAALYifAAAAFsRPgAAgK0IHwAAwFaEDwAAYCvCBwAAsBXhAwAA2IrwAQAAbOVT+OjevbscDke9ZcqUKZIkY4w8Ho9iYmLkcrk0fPhw7dy5s0kKBwAA/smn8LF161YdOnTIWrKysiRJN998syRp/vz5WrhwoRYtWqStW7fK7XZr5MiRKi8vb/zKAQCAX/IpfFxwwQVyu93W8vbbb+uiiy5SYmKijDFKT0/X7NmzlZycrISEBGVkZKiiokKZmZlNVT8AAPAzDT7no7q6Wq+99pruuOMOORwO5efnq7CwUElJSdYcp9OpxMRE5eTknHY7VVVVKisr81oAAEDr1eDwsWrVKpWUlOj222+XJBUWFkqSoqOjveZFR0db604lLS1NkZGR1hIbG9vQkgAAgB9ocPh4+eWXNWrUKMXExHiNOxwOr9fGmHpjJ5s1a5ZKS0utpaCgoKElAQAAPxDYkDft379f69ev14oVK6wxt9st6cQRkM6dO1vjRUVF9Y6GnMzpdMrpdDakDAAA4IcadORj6dKlioqK0ujRo62xuLg4ud1u6woY6cR5IdnZ2Ro6dOi5VwoAAFoFn4981NbWaunSpZo4caICA//zdofDoZSUFKWmpio+Pl7x8fFKTU1VSEiIxo8f36hFAwAA/+Vz+Fi/fr0OHDigO+64o966GTNmqLKyUpMnT1ZxcbEGDx6sdevWKTw8vFGKBQAA/s/n8JGUlCRjzCnXORwOeTweeTyec60LAAC0UjzbBQAA2IrwAQAAbEX4AAAAtiJ8AAAAWxE+AACArQgfAADAVoQPAABgK8IHAACwFeEDAADYivABAABsRfgAAAC2InwAAABbET4AAICtCB8AAMBWhA8AAGArwgcAALAV4QMAANiK8AEAAGxF+AAAALYifAAAAFsRPgAAgK0IHwAAwFaEDwAAYCvCBwAAsBXhAwAA2IrwAQAAbEX4AAAAtiJ8AAAAWxE+AACArQgfAADAVoQPAABgK5/Dx8GDB3XrrbeqY8eOCgkJ0WWXXaZt27ZZ640x8ng8iomJkcvl0vDhw7Vz585GLRoAAPgvn8JHcXGxrrrqKgUFBemdd97Rrl27tGDBArVr186aM3/+fC1cuFCLFi3S1q1b5Xa7NXLkSJWXlzd27QAAwA8F+jL5ySefVGxsrJYuXWqNde/e3fq7MUbp6emaPXu2kpOTJUkZGRmKjo5WZmam7rnnnsapGgAA+C2fjnysXr1agwYN0s0336yoqCgNGDBAS5Yssdbn5+ersLBQSUlJ1pjT6VRiYqJycnJOuc2qqiqVlZV5LQAAoPXyKXzs27dPixcvVnx8vNauXatJkybpvvvu06uvvipJKiwslCRFR0d7vS86Otpa931paWmKjIy0ltjY2Ib0AQAA/IRP4aO2tlaXX365UlNTNWDAAN1zzz26++67tXjxYq95DofD67Uxpt5YnVmzZqm0tNRaCgoKfGwBAAD4E5/CR+fOndWnTx+vsUsuuUQHDhyQJLndbkmqd5SjqKio3tGQOk6nUxEREV4LAABovXwKH1dddZX27NnjNfbFF1+oW7dukqS4uDi53W5lZWVZ66urq5Wdna2hQ4c2QrkAAMDf+XS1y/3336+hQ4cqNTVVv/jFL/Txxx/rpZde0ksvvSTpxMctKSkpSk1NVXx8vOLj45WamqqQkBCNHz++SRoAAAD+xafwccUVV2jlypWaNWuWHnvsMcXFxSk9PV0TJkyw5syYMUOVlZWaPHmyiouLNXjwYK1bt07h4eGNXjwAAPA/PoUPSbrhhht0ww03nHa9w+GQx+ORx+M5l7oAAEArxbNdAACArQgfAADAVoQPAABgK8IHAACwFeEDAADYivABAABsRfgAAAC2InwAAABbET4AAICtCB8AAMBWhA8AAGArwgcAALAV4QMAANiK8AEAAGxF+AAAALYifAAAAFsRPgAAgK0IHwAAwFaEDwAAYCvCBwAAsBXhAwAA2IrwAQAAbEX4AAAAtiJ8AAAAWxE+AACArQgfAADAVoQPAABgK8IHAACwFeEDAADYivABAABsRfgAAAC28il8eDweORwOr8XtdlvrjTHyeDyKiYmRy+XS8OHDtXPnzkYvGgAA+C+fj3xceumlOnTokLXk5eVZ6+bPn6+FCxdq0aJF2rp1q9xut0aOHKny8vJGLRoAAPgvn8NHYGCg3G63tVxwwQWSThz1SE9P1+zZs5WcnKyEhARlZGSooqJCmZmZjV44AADwTz6Hjy+//FIxMTGKi4vT2LFjtW/fPklSfn6+CgsLlZSUZM11Op1KTExUTk7OabdXVVWlsrIyrwUAALRePoWPwYMH69VXX9XatWu1ZMkSFRYWaujQofruu+9UWFgoSYqOjvZ6T3R0tLXuVNLS0hQZGWktsbGxDWgDAAD4C5/Cx6hRo/Szn/1Mffv21bXXXqs1a9ZIkjIyMqw5DofD6z3GmHpjJ5s1a5ZKS0utpaCgwJeSAACAnzmnS21DQ0PVt29fffnll9ZVL98/ylFUVFTvaMjJnE6nIiIivBYAANB6nVP4qKqq0u7du9W5c2fFxcXJ7XYrKyvLWl9dXa3s7GwNHTr0nAsFAACtQ6Avk3/729/qxhtv1IUXXqiioiL9/ve/V1lZmSZOnCiHw6GUlBSlpqYqPj5e8fHxSk1NVUhIiMaPH99U9QMAAD/jU/j46quvNG7cOH377be64IILdOWVV2rz5s3q1q2bJGnGjBmqrKzU5MmTVVxcrMGDB2vdunUKDw9vkuIBAID/8Sl8LFu27IzrHQ6HPB6PPB7PudQEAABaMZ7tAgAAbEX4AAAAtiJ8AAAAWxE+AACArQgfAADAVoQPAABgK8IHAACwFeEDAADYivABAABsRfgAAAC2InwAAABbET4AAICtCB8AAMBWhA8AAGArwgcAALAV4QMAANiK8AEAAGxF+AAAALYifAAAAFsRPgAAgK0IHwAAwFaEDwAAYCvCBwAAsBXhAwAA2IrwAQAAbEX4AAAAtiJ8AAAAWxE+AACArQgfAADAVoQPAABgq3MKH2lpaXI4HEpJSbHGjDHyeDyKiYmRy+XS8OHDtXPnznOtEwAAtBINDh9bt27VSy+9pH79+nmNz58/XwsXLtSiRYu0detWud1ujRw5UuXl5edcLAAA8H8NCh+HDx/WhAkTtGTJErVv394aN8YoPT1ds2fPVnJyshISEpSRkaGKigplZmY2WtEAAMB/NSh8TJkyRaNHj9a1117rNZ6fn6/CwkIlJSVZY06nU4mJicrJyTm3SgEAQKsQ6Osbli1bpk8//VRbt26tt66wsFCSFB0d7TUeHR2t/fv3n3J7VVVVqqqqsl6XlZX5WhIAAPAjPh35KCgo0LRp0/Taa6+pbdu2p53ncDi8Xhtj6o3VSUtLU2RkpLXExsb6UhIAAPAzPoWPbdu2qaioSAMHDlRgYKACAwOVnZ2tZ599VoGBgdYRj7ojIHWKiorqHQ2pM2vWLJWWllpLQUFBA1sBAAD+wKePXUaMGKG8vDyvsV/96lfq3bu3Zs6cqR49esjtdisrK0sDBgyQJFVXVys7O1tPPvnkKbfpdDrldDobWD4AAPA3PoWP8PBwJSQkeI2FhoaqY8eO1nhKSopSU1MVHx+v+Ph4paamKiQkROPHj2+8qgEAgN/y+YTTHzJjxgxVVlZq8uTJKi4u1uDBg7Vu3TqFh4c39q4AAIAfOufw8cEHH3i9djgc8ng88ng857ppAADQCvFsFwAAYCvCBwAAsBXhAwAA2IrwAQAAbEX4AAAAtiJ8AAAAWxE+AACArQgfAADAVoQPAABgK8IHAACwFeEDAADYivABAABsRfgAAAC2InwAAABbET4AAICtCB8AAMBWhA8AAGArwgcAALAV4QMAANiK8AEAAGxF+AAAALYifAAAAFsRPgAAgK0IHwAAwFaEDwAAYCvCBwAAsBXhAwAA2IrwAQAAbEX4AAAAtiJ8AAAAWxE+AACArXwKH4sXL1a/fv0UERGhiIgIDRkyRO+884613hgjj8ejmJgYuVwuDR8+XDt37mz0ogEAgP/yKXx07dpV8+bN0yeffKJPPvlEP/nJT3TTTTdZAWP+/PlauHChFi1apK1bt8rtdmvkyJEqLy9vkuIBAID/8Sl83Hjjjbr++uvVq1cv9erVS0888YTCwsK0efNmGWOUnp6u2bNnKzk5WQkJCcrIyFBFRYUyMzObqn4AAOBnGnzOx/Hjx7Vs2TIdOXJEQ4YMUX5+vgoLC5WUlGTNcTqdSkxMVE5OTqMUCwAA/F+gr2/Iy8vTkCFDdPToUYWFhWnlypXq06ePFTCio6O95kdHR2v//v2n3V5VVZWqqqqs12VlZb6WBAAA/IjPRz4uvvhi5ebmavPmzfrNb36jiRMnateuXdZ6h8PhNd8YU2/sZGlpaYqMjLSW2NhYX0sCAAB+xOfwERwcrJ49e2rQoEFKS0tT//799cwzz8jtdkuSCgsLveYXFRXVOxpyslmzZqm0tNRaCgoKfC0JAAD4kXO+z4cxRlVVVYqLi5Pb7VZWVpa1rrq6WtnZ2Ro6dOhp3+90Oq1Ld+sWAADQevl0zsfDDz+sUaNGKTY2VuXl5Vq2bJk++OADvfvuu3I4HEpJSVFqaqri4+MVHx+v1NRUhYSEaPz48U1V/3ljb9Hh5i7BJ+1Dg9Wlnau5ywAAtEA+hY9//etfuu2223To0CFFRkaqX79+evfddzVy5EhJ0owZM1RZWanJkyeruLhYgwcP1rp16xQeHt4kxZ8P2ocGyxUUoJTluc1dik9cQQFa/0AiAQQAUI/DGGOau4iTlZWVKTIyUqWlpY3+EcyOg6W64blNevveYUroEtmo225KB0sqVXykurnLOGt7iw4rZXmu332dAQAN58vvb58vtYX9urRzcQQBANBq8GA5AABgK8IHAACwFeEDAADYivABAABsRfgAAAC2InwAAABbET4AAICtCB8AAMBWhA8AAGArwgcAALAV4QMAANiK8AEAAGxF+AAAALbiqbaAHztYUqniI9XNXYZP2ocG85Rm4DxH+AD81MGSSl27IFuVx443dyk+cQUFaP0DiQQQ4DxG+AD8VPGRalUeO670Wy5Tz6iw5i7nrOwtOqyU5bkqPlJN+ADOY4QPwM/1jApTQpfI5i4DAM4aJ5wCAABbET4AAICtCB8AAMBWhA8AAGArwgcAALAV4QMAANiK8AEAAGxF+AAAALYifAAAAFsRPgAAgK0IHwAAwFaEDwAAYCvCBwAAsJVP4SMtLU1XXHGFwsPDFRUVpTFjxmjPnj1ec4wx8ng8iomJkcvl0vDhw7Vz585GLRoAAPgvn8JHdna2pkyZos2bNysrK0s1NTVKSkrSkSNHrDnz58/XwoULtWjRIm3dulVut1sjR45UeXl5oxcPAAD8T6Avk999912v10uXLlVUVJS2bdumq6++WsYYpaena/bs2UpOTpYkZWRkKDo6WpmZmbrnnnsar3IAAOCXzumcj9LSUklShw4dJEn5+fkqLCxUUlKSNcfpdCoxMVE5OTmn3EZVVZXKysq8FgAA0Ho1OHwYYzR9+nQNGzZMCQkJkqTCwkJJUnR0tNfc6Ohoa933paWlKTIy0lpiY2MbWhIAAPADDQ4fU6dO1eeff6433nij3jqHw+H12hhTb6zOrFmzVFpaai0FBQUNLQkAAPgBn875qHPvvfdq9erV2rhxo7p27WqNu91uSSeOgHTu3NkaLyoqqnc0pI7T6ZTT6WxIGQAAwA/5dOTDGKOpU6dqxYoVev/99xUXF+e1Pi4uTm63W1lZWdZYdXW1srOzNXTo0MapGAAA+DWfjnxMmTJFmZmZ+r//+z+Fh4db53FERkbK5XLJ4XAoJSVFqampio+PV3x8vFJTUxUSEqLx48c3SQNAYzlYUqniI9XNXcZZ21t0uLlLAIAG8Sl8LF68WJI0fPhwr/GlS5fq9ttvlyTNmDFDlZWVmjx5soqLizV48GCtW7dO4eHhjVIw0BQOllTq2gXZqjx2vLlL8YkrKEDtQ4ObuwwA8IlP4cMY84NzHA6HPB6PPB5PQ2sCbFd8pFqVx44r/ZbL1DMqrLnLOWvtQ4PVpZ2rucsAAJ806IRToLXqGRWmhC6RzV0GALRqPFgOAADYivABAABsRfgAAAC2InwAAABbccIpmow/3YfCn2oFAH9H+ECjax8aLFdQgFKW5zZ3KT7hnhkAYA/CBxpdl3YurX8g0a/uFipxzwwAsAvhA02iSzsXv8gBAKfECacAAMBWhA8AAGArwgcAALAV4QMAANiK8AEAAGxF+AAAALYifAAAAFsRPgAAgK0IHwAAwFaEDwAAYCvCBwAAsBXhAwAA2IrwAQAAbEX4AAAAtiJ8AAAAWxE+AACArQgfAADAVoQPAABgK8IHAACwFeEDAADYivABAABsRfgAAAC28jl8bNy4UTfeeKNiYmLkcDi0atUqr/XGGHk8HsXExMjlcmn48OHauXNnY9ULAAD8nM/h48iRI+rfv78WLVp0yvXz58/XwoULtWjRIm3dulVut1sjR45UeXn5ORcLAAD8X6Cvbxg1apRGjRp1ynXGGKWnp2v27NlKTk6WJGVkZCg6OlqZmZm65557zq1aAADg9xr1nI/8/HwVFhYqKSnJGnM6nUpMTFROTs4p31NVVaWysjKvBQAAtF6NGj4KCwslSdHR0V7j0dHR1rrvS0tLU2RkpLXExsY2ZkkAAKCFaZKrXRwOh9drY0y9sTqzZs1SaWmptRQUFDRFSQAAoIXw+ZyPM3G73ZJOHAHp3LmzNV5UVFTvaEgdp9Mpp9PZmGUAAIAWrFGPfMTFxcntdisrK8saq66uVnZ2toYOHdqYuwIAAH7K5yMfhw8f1t69e63X+fn5ys3NVYcOHXThhRcqJSVFqampio+PV3x8vFJTUxUSEqLx48c3auEAAMA/+Rw+PvnkE11zzTXW6+nTp0uSJk6cqFdeeUUzZsxQZWWlJk+erOLiYg0ePFjr1q1TeHh441UNAAD8ls/hY/jw4TLGnHa9w+GQx+ORx+M5l7oAAEArxbNdAACArQgfAADAVoQPAABgK8IHAACwFeEDAADYqlHvcAoArdXBkkoVH6lu7jJ80j40WF3auZq7DKAewgcA/ICDJZW6dkG2Ko8db+5SfOIKCtD6BxIJIGhxCB8A8AOKj1Sr8thxpd9ymXpGhTV3OWdlb9FhpSzPVfGRasIHWhzCBwCcpZ5RYUroEtncZQB+jxNOAQCArQgfAADAVoQPAABgK8IHAACwFSecArDd3qLDzV2CT/ytXtiLe8D4jvABwDbtQ4PlCgpQyvLc5i7FZ66gALUPDW7uMtDCcA+YhiF8ALBNl3YurX8g0e/+lSg1/78U0TJxD5iGIXwAsFWXdi5+iaPV4R4wvuGEUwAAYKvzLnzE6Fu1/TZPKilo7lIAADgvnVcfuwQdPqj1zgcVsrJKCgqRpnwstYtt7rIAADivnFdHPgKO/lshjioVDbhPOlYhVXzX3CUBAHDeOa/CR51jYV2auwQAAM5b52X4AAAAzYfwAQAAbEX4AAAAtiJ8AAAAW51Xl9oCwPnG3x6Kx23szw+EDwBohfz1IX7N/cAz2IPwAQCtkD8+xK8lPPAM9iB8AEArxUP80FKd3+Hj2y/+8/eQjidutV5ScOLOp3WvT+ds5wEAfOJP56n4U60tSZOFjxdeeEFPPfWUDh06pEsvvVTp6en68Y9/3FS780lN2w4nnu2y4u7/DAaFSLeukF5LPnHr9TM9+6WkQHr+Rz88DwBw1vz5PJX2ocHNXYZfaZLwsXz5cqWkpOiFF17QVVddpT/+8Y8aNWqUdu3apQsvvLApdumTY2ExJwJD3bNdvv3iRBD59osTgeLqB6WNT51Yf6pQUfHd2c0DAJw1fzxPReIKnYZokvCxcOFC3XnnnbrrrrskSenp6Vq7dq0WL16stLS0ptil79rFnj4wRJ5lkDjbeQCAs8J5KueHRg8f1dXV2rZtmx566CGv8aSkJOXk5NSbX1VVpaqqKut1aWmpJKmsrKyxS1P54SMqqzIn/jx5++WHpSojff3liT8PV57485+5J9Z933d7veeVH5aaoF4AABrb4fIy1VZV6HB5mcrKHI223brfq8aYH55sGtnBgweNJPPhhx96jT/xxBOmV69e9ebPmTPHSGJhYWFhYWFpBUtBQcEPZoUmO+HU4fBOU8aYemOSNGvWLE2fPt16XVtbq3//+9/q2LHjKec3VFlZmWJjY1VQUKCIiIhG225LQG/+qTX3JrXu/ujNP7Xm3qTm788Yo/LycsXExPzg3EYPH506dVJAQIAKCwu9xouKihQdHV1vvtPplNPp9Bpr165dY5dliYiIaJU/dBK9+avW3JvUuvujN//UmnuTmre/yMjIs5rX6A+WCw4O1sCBA5WVleU1npWVpaFDhzb27gAAgJ9pko9dpk+frttuu02DBg3SkCFD9NJLL+nAgQOaNGlSU+wOAAD4kSYJH7fccou+++47PfbYYzp06JASEhL0t7/9Td26dWuK3Z0Vp9OpOXPm1PuIpzWgN//UmnuTWnd/9OafWnNvkn/15zDmbK6JAQAAaByNfs4HAADAmRA+AACArQgfAADAVoQPAABgq/MifLzwwguKi4tT27ZtNXDgQP3973+3df8bN27UjTfeqJiYGDkcDq1atcprvTFGHo9HMTExcrlcGj58uHbu3Ok1p6qqSvfee686deqk0NBQ/dd//Ze++uorrznFxcW67bbbFBkZqcjISN12220qKSnxmnPgwAHdeOONCg0NVadOnXTfffeputr7CZJ5eXlKTEyUy+VSly5d9Nhjj53yXv1paWm64oorFB4erqioKI0ZM0Z79uxpFb1J0uLFi9WvXz/rhj1DhgzRO++80yp6+760tDQ5HA6lpKT4fX8ej0cOh8Nrcbvdft9XnYMHD+rWW29Vx44dFRISossuu0zbtm1rFf1179693vfO4XBoypQpft9bTU2NHnnkEcXFxcnlcqlHjx567LHHVFtba83x5/581qAHuPiRZcuWmaCgILNkyRKza9cuM23aNBMaGmr2799vWw1/+9vfzOzZs81bb71lJJmVK1d6rZ83b54JDw83b731lsnLyzO33HKL6dy5sykrK7PmTJo0yXTp0sVkZWWZTz/91FxzzTWmf//+pqamxprz05/+1CQkJJicnByTk5NjEhISzA033GCtr6mpMQkJCeaaa64xn376qcnKyjIxMTFm6tSp1pzS0lITHR1txo4da/Ly8sxbb71lwsPDzdNPP12vr+uuu84sXbrU7Nixw+Tm5prRo0ebCy+80Bw+fNjvezPGmNWrV5s1a9aYPXv2mD179piHH37YBAUFmR07dvh9byf7+OOPTffu3U2/fv3MtGnT/P57N2fOHHPppZeaQ4cOWUtRUZHf92WMMf/+979Nt27dzO233262bNli8vPzzfr1683evXtbRX9FRUVe37esrCwjyWzYsMHve/v9739vOnbsaN5++22Tn59v3nzzTRMWFmbS09NbxffOV60+fPzoRz8ykyZN8hrr3bu3eeihh5qlnu+Hj9raWuN2u828efOssaNHj5rIyEjz4osvGmOMKSkpMUFBQWbZsmXWnIMHD5o2bdqYd9991xhjzK5du4wks3nzZmvORx99ZCSZ//f//p8x5kQIatOmjTl48KA154033jBOp9OUlpYaY4x54YUXTGRkpDl69Kg1Jy0tzcTExJja2toz9lZUVGQkmezs7FbXW5327dub//mf/2k1vZWXl5v4+HiTlZVlEhMTrfDhz/3NmTPH9O/f/5T9+nNfxhgzc+ZMM2zYsFP21hr6+75p06aZiy66yNTW1vp9b6NHjzZ33HGH11hycrK59dZbjTGt73v3Q1r1xy7V1dXatm2bkpKSvMaTkpKUk5PTTFV5y8/PV2FhoVeNTqdTiYmJVo3btm3TsWPHvObExMQoISHBmvPRRx8pMjJSgwcPtuZceeWVioyM9JqTkJDg9dCf6667TlVVVdZh248++kiJiYleN6m57rrr9PXXX+uf//znGXspLS2VJHXo0KHV9Xb8+HEtW7ZMR44c0ZAhQ1pNb1OmTNHo0aN17bXXeo37e39ffvmlYmJiFBcXp7Fjx2rfvn2toq/Vq1dr0KBBuvnmmxUVFaUBAwZoyZIl1np/7+9k1dXVeu2113THHXfI4XD4fW/Dhg3Te++9py+++EKS9Nlnn2nTpk26/vrrJbWu793ZaNXh49tvv9Xx48frPdAuOjq63oPvmktdHWeqsbCwUMHBwWrfvv0Z50RFRdXbflRUlNec7++nffv2Cg4OPuOcutdn+poZYzR9+nQNGzZMCQkJraa3vLw8hYWFyel0atKkSVq5cqX69OnTKnpbtmyZPv30U6WlpdVb58/9DR48WK+++qrWrl2rJUuWqLCwUEOHDtV3333n131J0r59+7R48WLFx8dr7dq1mjRpku677z69+uqrXu/x1/5OtmrVKpWUlOj2229vFb3NnDlT48aNU+/evRUUFKQBAwYoJSVF48aNaxX9+apJbq/e0jgcDq/Xxph6Y82tITV+f86p5jfGHPP/n2B0pnqmTp2qzz//XJs2baq3zp97u/jii5Wbm6uSkhK99dZbmjhxorKzs8+4PX/oraCgQNOmTdO6devUtm3b09bqj/2NGjXK+nvfvn01ZMgQXXTRRcrIyNCVV17pt31JUm1trQYNGqTU1FRJ0oABA7Rz504tXrxYv/zlL8+4TX/o72Qvv/yyRo0aVe/x7P7a2/Lly/Xaa68pMzNTl156qXJzc5WSkqKYmBhNnDjR7/vzVas+8tGpUycFBATUS2lFRUX1El1zqTsL/0w1ut1uVVdXq7i4+Ixz/vWvf9Xb/jfffOM15/v7KS4u1rFjx844p6ioSFL9RF7n3nvv1erVq7VhwwZ17dq1VfUWHBysnj17atCgQUpLS1P//v31zDPP+H1v27ZtU1FRkQYOHKjAwEAFBgYqOztbzz77rAIDA0/7Lxx/6e9koaGh6tu3r7788ku//7517txZffr08Rq75JJLdODAAWt7/txfnf3792v9+vW66667rDF/7+3BBx/UQw89pLFjx6pv37667bbbdP/991tHHv29P1+16vARHBysgQMHKisry2s8KytLQ4cObaaqvMXFxcntdnvVWF1drezsbKvGgQMHKigoyGvOoUOHtGPHDmvOkCFDVFpaqo8//tias2XLFpWWlnrN2bFjhw4dOmTNWbdunZxOpwYOHGjN2bhxo9clV+vWrVNMTIy6d+/uVbsxRlOnTtWKFSv0/vvvKy4urtX0djrGGFVVVfl9byNGjFBeXp5yc3OtZdCgQZowYYJyc3PVo0cPv+7vZFVVVdq9e7c6d+7s99+3q666qt7l7F988YX10E5/76/O0qVLFRUVpdGjR1tj/t5bRUWF2rTx/pUbEBBgXWrr7/357JxPWW3h6i61ffnll82uXbtMSkqKCQ0NNf/85z9tq6G8vNxs377dbN++3UgyCxcuNNu3b7cu9503b56JjIw0K1asMHl5eWbcuHGnvLyqa9euZv369ebTTz81P/nJT055eVW/fv3MRx99ZD766CPTt2/fU15eNWLECPPpp5+a9evXm65du3pdXlVSUmKio6PNuHHjTF5enlmxYoWJiIg45eVVv/nNb0xkZKT54IMPvC6Pq6iosOb4a2/GGDNr1iyzceNGk5+fbz7//HPz8MMPmzZt2ph169b5fW+ncvLVLv7c3wMPPGA++OADs2/fPrN582Zzww03mPDwcOu/eX/ty5gTl0UHBgaaJ554wnz55Zfm9ddfNyEhIea1117z++9bnePHj5sLL7zQzJw5s946f+5t4sSJpkuXLtaltitWrDCdOnUyM2bMaBX9+arVhw9jjHn++edNt27dTHBwsLn88sutS0HtsmHDBiOp3jJx4kRjzIlLrObMmWPcbrdxOp3m6quvNnl5eV7bqKysNFOnTjUdOnQwLpfL3HDDDebAgQNec7777jszYcIEEx4ebsLDw82ECRNMcXGx15z9+/eb0aNHG5fLZTp06GCmTp3qdSmVMcZ8/vnn5sc//rFxOp3G7XYbj8dzykurTtWTJLN06VJrjr/2Zowxd9xxh/Vzc8EFF5gRI0ZYwcPfezuV74cPf+2v7t4IQUFBJiYmxiQnJ5udO3f6fV91/vrXv5qEhATjdDpN7969zUsvveS13t/7W7t2rZFk9uzZU2+dP/dWVlZmpk2bZi688ELTtm1b06NHDzN79mxTVVXVKvrzlcOYxrpdGQAAwA9r1ed8AACAlofwAQAAbEX4AAAAtiJ8AAAAWxE+AACArQgfAADAVoQPAABgK8IHAACwFeEDAADYivABAABsRfgAAAC2InwAAABb/X8NU9LypxBpMQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THIS is the FINDSTILLBROKEN code\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%1000 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "463db75a-694e-4c7d-9693-e10f3b4d65db",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before addressing enclaves, let's triage the discontinuous HDs\n",
      "Now work on dropping smallish disconnected pieces that would keep us above 742387 district pop vs 781460 target\n",
      "we'll also trim any HD with total islands' pop <= 15629\n",
      "working on HD 25\n",
      "working on HD 1057\n",
      "Out of 194 discontig HDs 194 had discontig HDs.\n",
      "Of these, 0 won't be under 742387 after all minor discontigys were shed, while 194 would be too underpopped if we trimmed the discontig pieces\n",
      "here is adjusted pop by original pop for those we trimmed and those we didn't (x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, reclassify after the trimming\n",
      "There are 194 HDs still with both discontinuity problems\n",
      "Additionally, 0 of the originally discontig HDs are now contig but still have an enclave\n"
     ]
    }
   ],
   "source": [
    "print(\"Before addressing enclaves, let's triage the discontinuous HDs\")\n",
    "#this is the CANTRIM code####\n",
    "\n",
    "#for t in sPgenerator:\n",
    "#    isContig, smallP = isContiguous(filledHDvtdList[t],unitNbrs)\n",
    "#    if isContig:\n",
    "#        print(\"oops!\",t)\n",
    "maxNudgeDownPop = int(0.02 * aDP)\n",
    "minPostFixPop = int(0.95 * aDP)\n",
    "print(\"Now work on dropping smallish disconnected pieces that would keep us above\",minPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "print(\"we'll also trim any HD with total islands' pop <=\",maxNudgeDownPop)\n",
    "nOrigIslands = [0 for t in range(nHDs)]\n",
    "totalIslandPop = [0. for t in range(nHDs)]\n",
    "tryToTrim, canTrim, cantTrim = list(), list(), list()\n",
    "for jj, t in enumerate(sPgenerator):\n",
    "    if jj%100 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    currList, shedList, shedPop = HDunitList[t].copy(), list(),  0.\n",
    "    done,newList = isContiguous(currList,unitNbrs)\n",
    "    if not done:\n",
    "        tryToTrim.append(t)\n",
    "        while not done:\n",
    "            shedList += newList\n",
    "            shedPop += np.sum( [unitPop[u] for u in newList] )\n",
    "            currList = list (  set(currList).difference(set(newList ))  )\n",
    "            done, newList = isContiguous(currList, unitNbrs)\n",
    "            nOrigIslands[t] +=1\n",
    "            \n",
    "        totalIslandPop[t] = shedPop            \n",
    "        if shedPop <= maxNudgeDownPop or HDvPop[t] - shedPop >= minPostFixPop:\n",
    "            canTrim.append(t)\n",
    "            HDvPop[t] -= shedPop\n",
    "            HDunitList[t] = list (set(HDunitList[t]).difference(set(shedList)) )\n",
    "        else:\n",
    "            cantTrim.append(t)\n",
    "print(\"Out of\",len(sPgenerator),\"discontig HDs\",len(tryToTrim),\"had discontig HDs.\")\n",
    "print(\"Of these,\",len(canTrim),\"won't be under\",minPostFixPop,\"after all minor discontigys were shed, while\",\n",
    "      len(cantTrim),\"would be too underpopped if we trimmed the discontig pieces\")\n",
    "plt.scatter([HDvPop[t] + totalIslandPop[t] for t in canTrim],[HDvPop[t] for t in canTrim])\n",
    "plt.scatter([HDvPop[t]                     for t in cantTrim],[HDvPop[t] for t in cantTrim],marker=\"x\")\n",
    "print(\"here is adjusted pop by original pop for those we trimmed and those we didn't (x)\")\n",
    "plt.plot([0.9*aDP, 1.1*aDP],[aDP, aDP], ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, reclassify after the trimming\")\n",
    "badDiscoList, stillHasEnclave = list(), list()\n",
    "for t in sPgenerator:    \n",
    "    unbroken, noEnclave, __, ____ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if not unbroken:\n",
    "        badDiscoList.append(t)  #will do a major triage on these later\n",
    "    if unbroken and not noEnclave:\n",
    "        stillHasEnclave.append(t)\n",
    "print(\"There are\",len(badDiscoList),\"HDs still with both discontinuity problems\")\n",
    "print(\"Additionally,\",len(stillHasEnclave),\"of the originally discontig HDs are now contig but still have an enclave\")\n",
    "eLgenerator += stillHasEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "39d37c15-759f-462c-b503-047b6b5e1554",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "307 15 194\n",
      "{516, 8, 10, 523, 22, 23, 24, 26, 27, 29, 2080, 547, 37, 38, 551, 40, 553, 555, 557, 558, 561, 1587, 564, 565, 1591, 60, 63, 67, 69, 2125, 90, 91, 611, 612, 614, 617, 623, 633, 1154, 132, 135, 647, 652, 661, 1701, 1202, 1203, 1204, 1205, 1207, 696, 697, 698, 1211, 1213, 1726, 1216, 1217, 1219, 196, 1221, 1220, 1223, 718, 1233, 1234, 724, 729, 730, 731, 732, 1753, 1251, 1252, 1274, 1276, 253, 768, 769, 1282, 771, 773, 774, 263, 783, 1301, 790, 792, 1316, 1318, 1321, 1322, 1323, 824, 1340, 1342, 1348, 838, 330, 331, 849, 850, 861, 1382, 1385, 1387, 1389, 1390, 1392, 1393, 1394, 1409, 1921, 1421, 1437, 416, 1958, 1978, 1979, 1996, 2004, 478, 479, 2020, 487, 490, 493, 2029}\n"
     ]
    }
   ],
   "source": [
    "print(len(discontigOnly),len(bothProblems), len(badDiscoList))\n",
    "print((set(discontigOnly).union(set(bothProblems)) ) .difference(set(badDiscoList)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e16dfee-b5bc-4863-8bed-9a6b22290aaa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "2e54bac4-c327-473d-bbb4-257486bddd20",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\n",
      "Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\n",
      "This takes about 3sec per HD :-( The total number of HDs to fix is 194\n",
      "swell-generating HD 25 our 1 th HD we must regrow out of 194 0 sec elapsed\n",
      "swell-generating HD 554 our 21 th HD we must regrow out of 194 33 sec elapsed\n",
      "swell-generating HD 918 our 41 th HD we must regrow out of 194 80 sec elapsed\n",
      "swell-generating HD 960 our 61 th HD we must regrow out of 194 178 sec elapsed\n",
      "swell-generating HD 1001 our 81 th HD we must regrow out of 194 288 sec elapsed\n",
      "swell-generating HD 1057 our 101 th HD we must regrow out of 194 385 sec elapsed\n",
      "swell-generating HD 1089 our 121 th HD we must regrow out of 194 630 sec elapsed\n",
      "swell-generating HD 1128 our 141 th HD we must regrow out of 194 831 sec elapsed\n",
      "swell-generating HD 1397 our 161 th HD we must regrow out of 194 959 sec elapsed\n",
      "swell-generating HD 1746 our 181 th HD we must regrow out of 194 1155 sec elapsed\n"
     ]
    }
   ],
   "source": [
    "#THIS IS THE MUSTSPAWN code block\n",
    "print(\"And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\")\n",
    "print(\"Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\")\n",
    "print(\"This takes about 3sec per HD :-( The total number of HDs to fix is\",len(badDiscoList))\n",
    "avgDiam = MAP.area**0.5 / float(nDistricts)\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(badDiscoList):\n",
    "    if i%20 == 0:\n",
    "        print(\"swell-generating HD\",t,\"our\",i+1,\"th HD we must regrow out of\",len(badDiscoList),int(time.time()-startTime),\"sec elapsed\" )\n",
    "    notInCluster = True\n",
    "    for jj, geo in enumerate(CCBgeom):  #swell in-corner HDs from the corner cluster\n",
    "        if geo.contains(hdCP[t]):\n",
    "            starterU = allUnits.index(jj+0.25)\n",
    "            notInCluster = False\n",
    "    if notInCluster:\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = [starterU]  #we used getContigFromStarter(starterU, HDvtdList[t], unitNbrs) in above code block\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    gap = aDP - contigPop\n",
    "    origGap = gap\n",
    "    currList, addedList = contigUs.copy(), list()\n",
    "    adjoiners = getAdjoiners(currList, unitNbrs)\n",
    "    nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "    nearHDscore = [ (0.2*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDpoly[t]) + \n",
    "        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] for uu in adjoiners ]\n",
    "    stillGoing = True\n",
    "    \n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd:\n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else: #we selected the best unit to add legally\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((0.1*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDpoly[t]) + \n",
    "                                                                        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] )\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "        unitUse[u] -= HDweight[t] * nDistricts       \n",
    "    HDunitList[t] = contigUs + addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b78199da-ac7c-4cfb-ac0b-26363a8a8c56",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prev avg use and its sd are 0.93377 0.10229\n",
      "defining and displaying current unit use after most recent manipulations; compare to original farther above.\n",
      "current avg use and its sd are 0.96228 0.10718\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"prev avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "print(\"defining and displaying current unit use after most recent manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "a2242181-3a07-40b7-ae56-bbe47bd2dc79",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final check -- any more disco's ?\n",
      "working on HD 0 time is now 0 sec\n",
      "working on HD 502 time is now 1 sec\n",
      "working on HD 1004 time is now 2 sec\n",
      "working on HD 1506 time is now 6 sec\n",
      "working on HD 2006 time is now 8 sec\n",
      "out of 2151 total HDs, there were 2143 0 8 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 8 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"(not-so-)final check -- any more disco's ?\")\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "de4d8af8-cc64-4215-9b95-f4c6a2bd03af",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "savedAgainUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "f3bcd6a8-40b6-4764-a0d1-6814e3286cb8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking on our corner clusters and unit county usage\n",
      "usage, unit no, unit pop, type (0.5 = unit county, 0.25 = cluster) ...\n",
      "0.72841     1858     263851 0.25\n"
     ]
    }
   ],
   "source": [
    "print(\"checking on our corner clusters and unit county usage\")\n",
    "print(\"usage, unit no, unit pop, type (0.5 = unit county, 0.25 = cluster) ...\")\n",
    "for u in range(nUnits):\n",
    "    if int(allUnits[u]) != allUnits[u] :\n",
    "        print(r5(unitUse[u]),\"   \",u,\"   \",int(unitPop[u]), allUnits[u]%1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1e983c03-777a-4e81-bed0-34a620175f83",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let's visualize over-used and underused units, < 0.75 or > 1.25\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here is the histogram of HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "minUnitUse, maxUnitUse = 0.75, 1.25\n",
    "print(\"let's visualize over-used and underused units, <\",minUnitUse,\"or >\",maxUnitUse)\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minUnitUse:\n",
    "        plotPoly(unitGeom[u],0.2)\n",
    "        plotCenter(\"u\",unitCP[u])\n",
    "    if unitUse[u] > maxUnitUse:\n",
    "        plotPoly(unitGeom[u], 1.5)\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"and here is the histogram of HD pops\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins = [0, 0.6*aDP, 0.7*aDP, 0.85*aDP, 0.9*aDP, 0.95*aDP,\n",
    "         0.98*aDP, aDP, 1.02*aDP, 1.05*aDP, 1.1*aDP, 1.15*aDP, 1.3*aDP, 1.5*aDP, 2.0*aDP])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "4c027656-0f83-4c5b-b9be-59d3305dc1de",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's fill in all enclaves that won't put us over 820533 district pop vs 781460 target\n",
      "working on enclave-y HD 1203 . We have evaluated 0 of 332 enclavy HDs.Time is now 0\n",
      "Out of 332 HDs with enclaves 8 had enclaves.\n",
      "Of these, 8 wouldn't be over 820533 if all enclaves filled, while 0 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
      "image/png": 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cqJ07d8bVRKNRhUIhOY4jx3EUCoXU1NQUV7N9+3aVlJQoJSVFmZmZmj17ttra2npzSAAAwGK9Cjrr16/Xz372M1VVVam8vFxfffWVCgoKdOjQobi6oqIiRSIRd1m7dm3c9jlz5mjVqlUqKytTRUWFDh48qOLiYnV0dLg1U6ZMUW1trcLhsMLhsGpraxUKhdztHR0dmjBhgg4dOqSKigqVlZVp5cqVmjt37un0AQAA2MicgcbGRiPJrF+/3l03bdo0c/vtt5/wd5qamszAgQNNWVmZu27Xrl0mISHBhMNhY4wxW7ZsMZJMVVWVW1NZWWkkmU8//dQYY8zatWtNQkKC2bVrl1vz6quvGq/Xa2KxWI/GH4vFjKQe1wMAgL7Xm/fvMzpHJxaLSZIyMjLi1r///vvy+Xy69NJLNWPGDDU2Nrrbampq1N7eroKCAnddIBBQMBjUhg0bJEmVlZVyHEejRo1ya0aPHi3HceJqgsGgAoGAW1NYWKjW1lbV1NR0O97W1lY1NzfHLQAAwF6nHXSMMXrooYd0ww03KBgMuuvHjx+vFStW6N1339XTTz+tDz/8ULfccotaW1slSQ0NDUpKSlJ6enrc/rKystTQ0ODW+Hy+Lo/p8/niarKysuK2p6enKykpya053qJFi9xzfhzHUU5OzukePgAA6AcGnO4vlpaW6uOPP1ZFRUXc+smTJ7v/DgaDuvbaazV06FCtWbNGd9xxxwn3Z4yRx+Nxfz7232dSc6xHHnlEDz30kPtzc3MzYQcAAIud1ozOrFmz9MYbb+i9997TkCFDTlqbnZ2toUOHatu2bZIkv9+vtrY2RaPRuLrGxkZ3hsbv92vPnj1d9rV37964muNnbqLRqNrb27vM9Bzl9XqVlpYWtwAAAHv1KugYY1RaWqrXXntN7777roYNG3bK39m3b5927Nih7OxsSVJeXp4GDhyo8vJytyYSiaiurk5jxoyRJOXn5ysWi6m6utqt2bhxo2KxWFxNXV2dIpGIW7Nu3Tp5vV7l5eX15rAAAIClPMYY09PiBx98UH/605/0l7/8RSNGjHDXO46j5ORkHTx4UAsWLNCdd96p7OxsffHFF3r00Ue1fft2bd26VampqZKkmTNn6s0339TLL7+sjIwMzZs3T/v27VNNTY0SExMlHTnXZ/fu3Vq2bJkk6b777tPQoUO1evVqSUcuL7/qqquUlZWlJ598Uvv379f06dM1adIkPffccz06nubmZjmOo1gsxuwOAAD9RK/ev3tzOZekbpeXXnrJGGPM//3f/5mCggJz8cUXm4EDB5pLLrnETJs2zWzfvj1uP4cPHzalpaUmIyPDJCcnm+Li4i41+/btM1OnTjWpqakmNTXVTJ061USj0biaL7/80kyYMMEkJyebjIwMU1paalpaWnp8PFxeDgBA/9Ob9+9ezejYhhkdAAD6n968f/NdVwAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrDejrAQAATk9Hp1F1/X41HmiRL3WQrhuWocQET18PC/hG6dWMzqJFi/SDH/xAqamp8vl8mjRpkj777LO4GmOMFixYoEAgoOTkZN1000365JNP4mpaW1s1a9YsZWZmKiUlRRMnTtTOnTvjaqLRqEKhkBzHkeM4CoVCampqiqvZvn27SkpKlJKSoszMTM2ePVttbW29OSQA6JfCdRHd8Pi7uvuFKv1rWa3ufqFKNzz+rsJ1kb4eGvCN0qugs379ev3sZz9TVVWVysvL9dVXX6mgoECHDh1ya5544gk988wzWrJkiT788EP5/X6NGzdOBw4ccGvmzJmjVatWqaysTBUVFTp48KCKi4vV0dHh1kyZMkW1tbUKh8MKh8Oqra1VKBRyt3d0dGjChAk6dOiQKioqVFZWppUrV2ru3Lln0g8A+MYL10U0c/kmRWItcesbYi2auXwTYQc4hscYY073l/fu3Sufz6f169frxhtvlDFGgUBAc+bM0cMPPyzpyOxNVlaWHn/8cd1///2KxWK6+OKL9corr2jy5MmSpN27dysnJ0dr165VYWGhtm7dqtzcXFVVVWnUqFGSpKqqKuXn5+vTTz/ViBEj9NZbb6m4uFg7duxQIBCQJJWVlWn69OlqbGxUWlraKcff3Nwsx3EUi8V6VA8Afa2j0+iGx9/tEnKO8kjyO4NU8fAtfIwFa/Xm/fuMTkaOxWKSpIyMDElSfX29GhoaVFBQ4NZ4vV6NHTtWGzZskCTV1NSovb09riYQCCgYDLo1lZWVchzHDTmSNHr0aDmOE1cTDAbdkCNJhYWFam1tVU1NTbfjbW1tVXNzc9wCAP1Jdf3+E4YcSTKSIrEWVdfvP3+DAr7BTjvoGGP00EMP6YYbblAwGJQkNTQ0SJKysrLiarOystxtDQ0NSkpKUnp6+klrfD5fl8f0+XxxNcc/Tnp6upKSktya4y1atMg958dxHOXk5PT2sAGgTzUeOHHIOZ06wHanHXRKS0v18ccf69VXX+2yzeOJny41xnRZd7zja7qrP52aYz3yyCOKxWLusmPHjpOOCQC+aXypg85qHWC70wo6s2bN0htvvKH33ntPQ4YMcdf7/X5J6jKj0tjY6M6++P1+tbW1KRqNnrRmz549XR537969cTXHP040GlV7e3uXmZ6jvF6v0tLS4hYA6E+uG5ahbGeQTvSno0dStnPkUnMAvQw6xhiVlpbqtdde07vvvqthw4bFbR82bJj8fr/Ky8vddW1tbVq/fr3GjBkjScrLy9PAgQPjaiKRiOrq6tya/Px8xWIxVVdXuzUbN25ULBaLq6mrq1Mk8vXVBevWrZPX61VeXl5vDgsA+o3EBI/ml+RKUpewc/Tn+SW5nIgM/D+9uurqwQcf1J/+9Cf95S9/0YgRI9z1juMoOTlZkvT4449r0aJFeumllzR8+HAtXLhQ77//vj777DOlpqZKkmbOnKk333xTL7/8sjIyMjRv3jzt27dPNTU1SkxMlCSNHz9eu3fv1rJlyyRJ9913n4YOHarVq1dLOnJ5+VVXXaWsrCw9+eST2r9/v6ZPn65Jkybpueee69HxcNUVgP4qXBfRY6u3xJ2YnO0M0vySXBUFs/twZMC516v3b9MLOnJCf5flpZdecms6OzvN/Pnzjd/vN16v19x4441m8+bNcfs5fPiwKS0tNRkZGSY5OdkUFxeb7du3x9Xs27fPTJ061aSmpprU1FQzdepUE41G42q+/PJLM2HCBJOcnGwyMjJMaWmpaWlp6fHxxGIxI8nEYrHetAEAvhG+6ug0Gz7/h3n9/9tpNnz+D/NVR2dfDwk4L3rz/n1G99Hp75jRAQCg/zlv99EBAAD4JiPoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKzV66DzwQcfqKSkRIFAQB6PR6+//nrc9unTp8vj8cQto0ePjqtpbW3VrFmzlJmZqZSUFE2cOFE7d+6Mq4lGowqFQnIcR47jKBQKqampKa5m+/btKikpUUpKijIzMzV79my1tbX19pAAAICleh10Dh06pCuvvFJLliw5YU1RUZEikYi7rF27Nm77nDlztGrVKpWVlamiokIHDx5UcXGxOjo63JopU6aotrZW4XBY4XBYtbW1CoVC7vaOjg5NmDBBhw4dUkVFhcrKyrRy5UrNnTu3t4cEAAAsNaC3vzB+/HiNHz/+pDVer1d+v7/bbbFYTC+++KJeeeUV3XbbbZKk5cuXKycnR++8844KCwu1detWhcNhVVVVadSoUZKkF154Qfn5+frss880YsQIrVu3Tlu2bNGOHTsUCAQkSU8//bSmT5+uX/3qV0pLS+vtoQGAVTo6jarr96vxQIt8qYN03bAMJSZ4+npYwHnV66DTE++//758Pp++/e1va+zYsfrVr34ln88nSaqpqVF7e7sKCgrc+kAgoGAwqA0bNqiwsFCVlZVyHMcNOZI0evRoOY6jDRs2aMSIEaqsrFQwGHRDjiQVFhaqtbVVNTU1uvnmm7uMq7W1Va2tre7Pzc3N5+LwAaDPhesiemz1FkViLe66bGeQ5pfkqiiY3YcjA86vs34y8vjx47VixQq9++67evrpp/Xhhx/qlltucQNGQ0ODkpKSlJ6eHvd7WVlZamhocGuOBqNj+Xy+uJqsrKy47enp6UpKSnJrjrdo0SL3nB/HcZSTk3PGxwsA3zThuohmLt8UF3IkqSHWopnLNylcF+mjkQHn31kPOpMnT9aECRMUDAZVUlKit956S3/729+0Zs2ak/6eMUYez9dTqsf++0xqjvXII48oFou5y44dO3p6WADQL3R0Gj22eotMN9uOrnts9RZ1dHZXAdjnnF9enp2draFDh2rbtm2SJL/fr7a2NkWj0bi6xsZGd4bG7/drz549Xfa1d+/euJrjZ26i0aja29u7zPQc5fV6lZaWFrcAgE2q6/d3mck5lpEUibWoun7/+RsU0IfOedDZt2+fduzYoezsI58J5+XlaeDAgSovL3drIpGI6urqNGbMGElSfn6+YrGYqqur3ZqNGzcqFovF1dTV1SkS+XoKdt26dfJ6vcrLyzvXhwUA30iNB04cck6nDujven0y8sGDB/X555+7P9fX16u2tlYZGRnKyMjQggULdOeddyo7O1tffPGFHn30UWVmZupHP/qRJMlxHN17772aO3euBg8erIyMDM2bN08jR450r8K67LLLVFRUpBkzZmjZsmWSpPvuu0/FxcUaMWKEJKmgoEC5ubkKhUJ68skntX//fs2bN08zZsxgpgbABcuXOuis1gH9Xa9ndD766CNdffXVuvrqqyVJDz30kK6++mr913/9lxITE7V582bdfvvtuvTSSzVt2jRdeumlqqysVGpqqruPZ599VpMmTdJdd92l66+/XhdddJFWr16txMREt2bFihUaOXKkCgoKVFBQoCuuuEKvvPKKuz0xMVFr1qzRoEGDdP311+uuu+7SpEmT9NRTT51JPwCgX7tuWIaynUE60UXkHh25+uq6YRnnc1hAn/EYYy7YM9Kam5vlOI5isRizQACscfSqK0lxJyUfDT9L77mGS8zRr/Xm/ZvvugIAyxQFs7X0nmvkd+I/nvI7gwg5uOCckxsGAgD6VlEwW+Ny/dwZGRc8gg4AWCoxwaP87w3u62EAfYqPrgAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgrQF9PQAAAGCfjk6j6vr9ajzQIl/qIF03LEOJCZ7zPg6CDgAAOKvCdRE9tnqLIrEWd122M0jzS3JVFMw+r2Pp9UdXH3zwgUpKShQIBOTxePT666/HbTfGaMGCBQoEAkpOTtZNN92kTz75JK6mtbVVs2bNUmZmplJSUjRx4kTt3LkzriYajSoUCslxHDmOo1AopKampria7du3q6SkRCkpKcrMzNTs2bPV1tbW20MCAABnSbguopnLN8WFHElqiLVo5vJNCtdFzut4eh10Dh06pCuvvFJLlizpdvsTTzyhZ555RkuWLNGHH34ov9+vcePG6cCBA27NnDlztGrVKpWVlamiokIHDx5UcXGxOjo63JopU6aotrZW4XBY4XBYtbW1CoVC7vaOjg5NmDBBhw4dUkVFhcrKyrRy5UrNnTu3t4cEAADOgo5Oo8dWb5HpZtvRdY+t3qKOzu4qzg2PMea0H83j8WjVqlWaNGmSpCOzOYFAQHPmzNHDDz8s6cjsTVZWlh5//HHdf//9isViuvjii/XKK69o8uTJkqTdu3crJydHa9euVWFhobZu3arc3FxVVVVp1KhRkqSqqirl5+fr008/1YgRI/TWW2+puLhYO3bsUCAQkCSVlZVp+vTpamxsVFpa2inH39zcLMdxFIvFelQPAABOrPJ/9+nuF6pOWffqjNHK/97g036c3rx/n9Wrrurr69XQ0KCCggJ3ndfr1dixY7VhwwZJUk1Njdrb2+NqAoGAgsGgW1NZWSnHcdyQI0mjR4+W4zhxNcFg0A05klRYWKjW1lbV1NR0O77W1lY1NzfHLQAA4OxoPNBy6qJe1J0NZzXoNDQ0SJKysrLi1mdlZbnbGhoalJSUpPT09JPW+Hy+Lvv3+XxxNcc/Tnp6upKSktya4y1atMg958dxHOXk5JzGUQIAgO74Uged1bqz4ZzcR8fjib98zBjTZd3xjq/prv50ao71yCOPKBaLucuOHTtOOiYAANBz1w3LULYzSCd6x/foyNVX1w3LOG9jOqtBx+/3S1KXGZXGxkZ39sXv96utrU3RaPSkNXv27Omy/71798bVHP840WhU7e3tXWZ6jvJ6vUpLS4tbAADA2ZGY4NH8klxJ6hJ2jv48vyT3vN5P56wGnWHDhsnv96u8vNxd19bWpvXr12vMmDGSpLy8PA0cODCuJhKJqK6uzq3Jz89XLBZTdXW1W7Nx40bFYrG4mrq6OkUiX1+mtm7dOnm9XuXl5Z3NwwIAAD1UFMzW0nuukd+J/3jK7wzS0nuuOe/30en1DQMPHjyozz//3P25vr5etbW1ysjI0CWXXKI5c+Zo4cKFGj58uIYPH66FCxfqoosu0pQpUyRJjuPo3nvv1dy5czV48GBlZGRo3rx5GjlypG677TZJ0mWXXaaioiLNmDFDy5YtkyTdd999Ki4u1ogRIyRJBQUFys3NVSgU0pNPPqn9+/dr3rx5mjFjBjM1AAD0oaJgtsbl+r8Rd0aW6aX33nvP6Mjl8HHLtGnTjDHGdHZ2mvnz5xu/32+8Xq+58cYbzebNm+P2cfjwYVNaWmoyMjJMcnKyKS4uNtu3b4+r2bdvn5k6dapJTU01qampZurUqSYajcbVfPnll2bChAkmOTnZZGRkmNLSUtPS0tLjY4nFYkaSicVivW0DAADoI715/z6j++j0d9xHBwCA/qfP7qMDAADwTULQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABr9frbywF8M3V0mm/GNwXDSjy/0F8RdAALhOsiemz1FkViLe66bGeQ5pfkqiiY3Ycjgw14fqE/46MroJ8L10U0c/mmuDchSWqItWjm8k0K10X6aGSwAc8v9HcEHaAf6+g0emz1Fpluth1d99jqLero7K4CODmeX7ABQQfox6rr93f5S/tYRlIk1qLq+v3nb1CwBs8v2ICgA/RjjQdO/CZ0OnXAsXh+wQYEHaAf86UOOqt1wLF4fsEGBB2gH7tuWIaynUE60UW+Hh25Oua6YRnnc1iwBM8v2ICgA/RjiQkezS/JlaQub0ZHf55fksv9TnBaeH7BBgQdoJ8rCmZr6T3XyO/Ef3zgdwZp6T3XcJ8TnBGeX+jvPMaYC/a6wObmZjmOo1gsprS0tL4eDnBGuHMtziWeX/gm6c37N3dGBiyRmOBR/vcG9/UwYCmeX+iv+OgKAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLX49vJzoKPTqLp+vxoPtMiXOkjXDctQYoKnr4d1QaD3AIBjEXTOsnBdRI+t3qJIrMVdl+0M0vySXBUFs/twZPaj9wCA4/HR1VkUroto5vJNcW+0ktQQa9HM5ZsUrov00cjsR+8BAN0560FnwYIF8ng8cYvf73e3G2O0YMECBQIBJScn66abbtInn3wSt4/W1lbNmjVLmZmZSklJ0cSJE7Vz5864mmg0qlAoJMdx5DiOQqGQmpqazvbh9FhHp9Fjq7fIdLPt6LrHVm9RR2d3FTgT9B4AcCLnZEbn8ssvVyQScZfNmze725544gk988wzWrJkiT788EP5/X6NGzdOBw4ccGvmzJmjVatWqaysTBUVFTp48KCKi4vV0dHh1kyZMkW1tbUKh8MKh8Oqra1VKBQ6F4fTI9X1+7vMJhzLSIrEWlRdv//8DeoCQe8BACdyTs7RGTBgQNwszlHGGP3617/Wf/zHf+iOO+6QJP3P//yPsrKy9Kc//Un333+/YrGYXnzxRb3yyiu67bbbJEnLly9XTk6O3nnnHRUWFmrr1q0Kh8OqqqrSqFGjJEkvvPCC8vPz9dlnn2nEiBHn4rBOqvHAid9oT6cOPUfvAQAnck5mdLZt26ZAIKBhw4bpxz/+sf7+979Lkurr69XQ0KCCggK31uv1auzYsdqwYYMkqaamRu3t7XE1gUBAwWDQramsrJTjOG7IkaTRo0fLcRy35nzzpQ46q3XoOXoPADiRsx50Ro0apT/+8Y96++239cILL6ihoUFjxozRvn371NDQIEnKysqK+52srCx3W0NDg5KSkpSenn7SGp/P1+WxfT6fW9Od1tZWNTc3xy1ny3XDMpTtDNKJLmT26MgVQNcNyzhrj4kj6D0A4ETOetAZP3687rzzTo0cOVK33Xab1qxZI+nIR1RHeTzxb0nGmC7rjnd8TXf1p9rPokWL3JOXHcdRTk5Oj46pJxITPJpfkntkbMdtO/rz/JJc7ulyDtB7AMCJnPPLy1NSUjRy5Eht27bNPW/n+FmXxsZGd5bH7/erra1N0Wj0pDV79uzp8lh79+7tMlt0rEceeUSxWMxdduzYcUbHdryiYLaW3nON/E78RyR+Z5CW3nMN93I5h+g9AKA75/yGga2trdq6dat++MMfatiwYfL7/SovL9fVV18tSWpra9P69ev1+OOPS5Ly8vI0cOBAlZeX66677pIkRSIR1dXV6YknnpAk5efnKxaLqbq6Wtddd50kaePGjYrFYhozZswJx+L1euX1es/l4aoomK1xuX7uztsH6D0A4HhnPejMmzdPJSUluuSSS9TY2Kj//u//VnNzs6ZNmyaPx6M5c+Zo4cKFGj58uIYPH66FCxfqoosu0pQpUyRJjuPo3nvv1dy5czV48GBlZGRo3rx57kdhknTZZZepqKhIM2bM0LJlyyRJ9913n4qLi/vkiqvjJSZ4lP+9wX09jAsSvQcAHOusB52dO3fq7rvv1j/+8Q9dfPHFGj16tKqqqjR06FBJ0i9+8QsdPnxYDz74oKLRqEaNGqV169YpNTXV3cezzz6rAQMG6K677tLhw4d166236uWXX1ZiYqJbs2LFCs2ePdu9OmvixIlasmTJ2T4cAADQj3mMMRfs7WKbm5vlOI5isZjS0tL6ejgAAKAHevP+zXddAQAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsdc6/AuKb7OgthM7mt5gDAIBz6+j7dk9uBXhBB50DBw5I0ln9FnMAAHB+HDhwQI7jnLTmgr4zcmdnp3bv3q3U1FR5PH3/xY/Nzc3KycnRjh07Lvg7NdOLr9GLePTja/Tia/Qinu39MMbowIEDCgQCSkg4+Vk4F/SMTkJCgoYMGdLXw+giLS3Nyifm6aAXX6MX8ejH1+jF1+hFPJv7caqZnKM4GRkAAFiLoAMAAKxF0PkG8Xq9mj9/vrxeb18Ppc/Ri6/Ri3j042v04mv0Ih79+NoFfTIyAACwGzM6AADAWgQdAABgLYIOAACwFkEHAABYi6DTC7t27dI999yjwYMH66KLLtJVV12lmpoaSVJ7e7sefvhhjRw5UikpKQoEAvrJT36i3bt3x+2jtbVVs2bNUmZmplJSUjRx4kTt3LkzriYajSoUCslxHDmOo1AopKampria7du3q6SkRCkpKcrMzNTs2bPV1tYWV7N582aNHTtWycnJ+s53vqNf/vKXPfpekDPtxfHuv/9+eTwe/frXv45bb0svpJ71Y+vWrZo4caIcx1FqaqpGjx6t7du3W9ePU/Xi4MGDKi0t1ZAhQ5ScnKzLLrtMS5cujduHDb347ne/K4/H02X52c9+JunInV0XLFigQCCg5ORk3XTTTfrkk0+s60NP+nGhvX6e6rlxrAvh9fOcM+iR/fv3m6FDh5rp06ebjRs3mvr6evPOO++Yzz//3BhjTFNTk7ntttvMn//8Z/Ppp5+ayspKM2rUKJOXlxe3nwceeMB85zvfMeXl5WbTpk3m5ptvNldeeaX56quv3JqioiITDAbNhg0bzIYNG0wwGDTFxcXu9q+++soEg0Fz8803m02bNpny8nITCARMaWmpWxOLxUxWVpb58Y9/bDZv3mxWrlxpUlNTzVNPPXXOe3GsVatWmSuvvNIEAgHz7LPPWteLnvbj888/NxkZGebnP/+52bRpk/nf//1f8+abb5o9e/ZY1Y+e9OKnP/2p+d73vmfee+89U19fb5YtW2YSExPN66+/blUvGhsbTSQScZfy8nIjybz33nvGGGMWL15sUlNTzcqVK83mzZvN5MmTTXZ2tmlubraqDz3px4X0+nmqXhzrQnj9PB8IOj308MMPmxtuuKFXv1NdXW0kmS+//NIYcyQMDRw40JSVlbk1u3btMgkJCSYcDhtjjNmyZYuRZKqqqtyayspKI8l8+umnxhhj1q5daxISEsyuXbvcmldffdV4vV4Ti8WMMcY8//zzxnEc09LS4tYsWrTIBAIB09nZ2cujj9fTXuzcudN85zvfMXV1dWbo0KFx/1Ft6YUxPevH5MmTzT333HPC7bb0oye9uPzyy80vf/nLuHXXXHON+c///E9jjD29ON6//uu/mu9973ums7PTdHZ2Gr/fbxYvXuxub2lpMY7jmN/97ndW9+GoY/vRHVtfP7vTXS8ulNfP84GPrnrojTfe0LXXXqt//ud/ls/n09VXX60XXnjhpL8Ti8Xk8Xj07W9/W5JUU1Oj9vZ2FRQUuDWBQEDBYFAbNmyQJFVWVspxHI0aNcqtGT16tBzHiasJBoMKBAJuTWFhoVpbW92PCCorKzV27Ni4m0UVFhZq9+7d+uKLL855Lzo7OxUKhfTzn/9cl19+eZd92NKLnvSjs7NTa9as0aWXXqrCwkL5fD6NGjVKr7/+unX96Mlz44YbbtAbb7yhXbt2yRij9957T3/7299UWFhoVS+O1dbWpuXLl+tf/uVf5PF4VF9fr4aGhrhj9Hq9Gjt2rDt+G/twon50x9bXz+N114sL6fXzfCDo9NDf//53LV26VMOHD9fbb7+tBx54QLNnz9Yf//jHbutbWlr07//+75oyZYr7hWoNDQ1KSkpSenp6XG1WVpYaGhrcGp/P12V/Pp8vriYrKytue3p6upKSkk5ac/TnozWnqye9ePzxxzVgwADNnj27233Y0gvp1P1obGzUwYMHtXjxYhUVFWndunX60Y9+pDvuuEPr1693x2FDP3ry3Pjtb3+r3NxcDRkyRElJSSoqKtLzzz+vG264wR2DDb041uuvv66mpiZNnz49bt/dPfaxY7OtD0cd34/j2fz6ebzuenEhvX6eDxf0t5f3Rmdnp6699lotXLhQknT11Vfrk08+0dKlS/WTn/wkrra9vV0//vGP1dnZqeeff/6U+zbGxP1V091fOGejxvy/k8dO9BdUT52qFzU1NfrNb36jTZs29fqx+lsvpFP3o7OzU5J0++2369/+7d8kSVdddZU2bNig3/3udxo7duwJ993f+tGT/ye//e1vVVVVpTfeeENDhw7VBx98oAcffFDZ2dm67bbbTrjv/taLY7344osaP3583F/OJ3rsUz1uf+7DUSfqh2T/6+fxju/Fhfb6eT4wo9ND2dnZys3NjVt32WWXxV01Ix35T3rXXXepvr5e5eXl7l8jkuT3+9XW1qZoNBr3O42NjW5C9vv92rNnT5fH37t3b1zN8Uk6Go2qvb39pDWNjY2Suv4V2Vun6sVf//pXNTY26pJLLtGAAQM0YMAAffnll5o7d66++93vuuOzoRfSqfuRmZmpAQMGnLTGln6cqheHDx/Wo48+qmeeeUYlJSW64oorVFpaqsmTJ+upp55yx2dDL4768ssv9c477+inP/2pu87v90vq+hfx8cdoUx+O6q4fR10Ir5/H6q4XF9rr5/lA0Omh66+/Xp999lncur/97W8aOnSo+/PR/6Tbtm3TO++8o8GDB8fV5+XlaeDAgSovL3fXRSIR1dXVacyYMZKk/Px8xWIxVVdXuzUbN25ULBaLq6mrq1MkEnFr1q1bJ6/Xq7y8PLfmgw8+iLtMcN26dQoEAu5/lnPVi1AopI8//li1tbXuEggE9POf/1xvv/22Vb3oST+SkpL0gx/84KQ1tvTjVL1ob29Xe3u7EhLiX3oSExPdmS9benHUSy+9JJ/PpwkTJrjrhg0bJr/fH3eMbW1tWr9+vTt+2/pwVHf9kC6c189jddeLC+3187w4H2c826C6utoMGDDA/OpXvzLbtm0zK1asMBdddJFZvny5McaY9vZ2M3HiRDNkyBBTW1sbd+lga2uru58HHnjADBkyxLzzzjtm06ZN5pZbbun2ksArrrjCVFZWmsrKSjNy5MhuLwm89dZbzaZNm8w777xjhgwZEndJYFNTk8nKyjJ333232bx5s3nttddMWlraWbkk8FS96M7xVw3Y0oue9uO1114zAwcONL///e/Ntm3bzHPPPWcSExPNX//6V6v60ZNejB071lx++eXmvffeM3//+9/NSy+9ZAYNGmSef/55q3phjDEdHR3mkksuMQ8//HCXbYsXLzaO45jXXnvNbN682dx9993dXl5uQx9O1Y8L6fXzVL3ojs2vn+cDQacXVq9ebYLBoPF6veaf/umfzO9//3t3W319vZHU7XLsvREOHz5sSktLTUZGhklOTjbFxcVm+/btcY+zb98+M3XqVJOammpSU1PN1KlTTTQajav58ssvzYQJE0xycrLJyMgwpaWlcZf/GWPMxx9/bH74wx8ar9dr/H6/WbBgwVm7HPBkvehOd/9RbemFMT3rx4svvmi+//3vm0GDBpkrr7wy7r4xxtjTj1P1IhKJmOnTp5tAIGAGDRpkRowYYZ5++um4x7elF2+//baRZD777LMu2zo7O838+fON3+83Xq/X3HjjjWbz5s1xNbb04agT9eNCe/08WS+6Y/vr57nmMaY/3d4QAACg5zhHBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABr/f+F93fecgZxJwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"CANFILL\" CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.05 * aDP)\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",maxPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "        \n",
    "for iii, t in enumerate(enclaveOnly):\n",
    "    if iii%100 == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(internals+edgers),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        #print(\"HD\",t,\"has HDvPop, totalEnclavePop of\",HDvPop[t], int(totalEnclavePop[t]),\"vs target aDP\",int(aDP) )\n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Out of\",len(internals+edgers),\"HDs with enclaves\",len(tryToFill),\"had enclaves.\")\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "e184533d-09e2-4ee2-ae1f-3372d5357228",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final check -- any more disco's ?\n",
      "working on HD 0 time is now 0 sec\n",
      "working on HD 502 time is now 1 sec\n",
      "working on HD 1004 time is now 3 sec\n",
      "working on HD 1506 time is now 5 sec\n",
      "working on HD 2006 time is now 7 sec\n",
      "out of 2151 total HDs, there were 2151 0 0 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 7 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"final check -- any more disco's ?\")\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "114705bb-9640-45f1-9f25-030030efaa63",
   "metadata": {},
   "outputs": [],
   "source": [
    "#below here -- working on tightening use distro while squaring up pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "5d7d8519-eb3d-4345-9ac6-ab888e61c348",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before patching, make another copy of the current HD lists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-patch avg use and its sd are 0.96234 0.10715\n"
     ]
    }
   ],
   "source": [
    "print(\"before patching, make another copy of the current HD lists\")\n",
    "latestHDlist = [HDunitList[t].copy() for t in range(nHDs)]   #More safekeeping\n",
    "latestHDpop, latestUnitUse = [0. for t in range(nHDs)], [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        latestHDpop[t] += unitPop[u]\n",
    "        latestUnitUse[u] += nDistricts * HDweight[t]\n",
    "latestUnitWeights, latestUnitDistro = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if latestUnitUse[u] > 0.1:\n",
    "        latestUnitDistro.append(latestUnitUse[u])\n",
    "        latestUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(latestUnitDistro, weights=latestUnitWeights, bins = 20, histtype = \"step\")\n",
    "plt.show()\n",
    "latestAvg, latestSD = getWeightedAvgAndSD(latestUnitDistro, latestUnitWeights)\n",
    "print(\"pre-patch avg use and its sd are\",r5(latestAvg),r5(latestSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "17caead2-8aba-4fc5-817e-53d440c5a704",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"ContigButOffPopUnit.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c5d76a90-4441-4e96-b9a7-7de433686812",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving pre-squaring HD vtd lists to file\n"
     ]
    }
   ],
   "source": [
    "print(\"OPTIONAL - saving pre-squaring HD vtd lists to file\")\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        for i,uu in enumerate(surrounders):\n",
    "            if u == uu:\n",
    "                unitVTDlist[u].append(surroundedVTDs[i])\n",
    "\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        vtdList[t] += unitVTDlist[u]\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"contigNotSquaredVTD.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "b1971853-bda7-49a4-9f25-1bdfd5e75aa2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 0\n",
      "working on avg unit-to-HDcp dist for HD 802\n",
      "working on avg unit-to-HDcp dist for HD 1606\n"
     ]
    }
   ],
   "source": [
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "f179943a-ef19-4036-a8c6-5ab7b1f97bf8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Restart\n",
    "HDunitList = [latestHDlist[t].copy() for t in range(nHDs)]\n",
    "unitUse = [0. for u in range(nUnits) ]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs) ]\n",
    "\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, weights = unitPop,bins = 50)\n",
    "plt.show()\n",
    "plt.hist([HDvPop[t] for t in popHDlist], bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "512b2e80-04a3-4bd5-9537-ba6a2b9d81cb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1858\n"
     ]
    }
   ],
   "source": [
    "print(list(barredJettisonSet)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "9854465d-3163-4fa2-a151-7862a1396df3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "how much HD weight could pick up our CCB as a neighbor?\n",
      "we will boost unitUse of 1858 from 0.7284103069779461 to 1.01354 by attaching to all neighboring HDs with pop after adding of 1.338 * aDP\n"
     ]
    }
   ],
   "source": [
    "print(\"how much HD weight could pick up our CCB as a neighbor?\")\n",
    "boostWeight, CCBu = 0., list(barredJettisonSet)[0]\n",
    "CCBnbrSet = set(unitNbrs[CCBu]) #set(get2nbrs([CCBu], unitNbrs))\n",
    "receivingList = list()\n",
    "for t in popHDlist:\n",
    "    if HDvPop[t] < 1.338*aDP - unitPop[CCBu] and CCBu not in HDunitList[t]:  #1.336\n",
    "        if len(CCBnbrSet.intersection(set(HDunitList[t]))) > 0:\n",
    "            boostWeight += HDweight[t] * nDistricts\n",
    "            receivingList.append(t)\n",
    "print(\"we will boost unitUse of\",CCBu,\"from\",unitUse[CCBu],\"to\",r5(unitUse[CCBu]+boostWeight),\"by attaching to all neighboring HDs\",\n",
    "     \"with pop after adding of 1.338 * aDP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "2f89cffc-2050-429c-93da-192fac45bd7d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for t in receivingList: \n",
    "    unbroken, noEnclave, sPL, ePL = enclaveCheck(HDunitList[t]+[CCBu],unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        HDunitList[t].append(CCBu)\n",
    "        HDvPop[t] += unitPop[CCBu]\n",
    "        unitUse[CCBu] += HDweight[t] * nDistricts\n",
    "print(\"After CCBu tack-on, its use is now\",r5(unitUse[CCBu])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "3ccce928-9660-4eca-894e-2c24fc727040",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in countyUnitList[11]:\n",
    "    if unitUse[u] < 0.95:\n",
    "        plotPoly(unitGeom[u], 5*(1.-unitUse[u]) )\n",
    "        if unitUse[u] < 0.9:\n",
    "            plotCenter(r3(unitUse[u]),unitGeom[u],6)\n",
    "plotPoly(countyGeom[11])\n",
    "plotPoly(unitGeom[1858])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "fac56b07-7327-495b-98bf-963588639baa",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPoly(unitGeom[1858])\n",
    "for u in unitNbrs[1858]:\n",
    "    plotPoly(unitGeom[u],0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "27b51cd0-8bb6-4b16-bb79-37498e59ce5c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now square up the 440 HDs with pop < 773646 to within 2992\n",
      "working on squaring up HD 13 time is now 0\n",
      "working on squaring up HD 984 time is now 17\n",
      "working on squaring up HD 1213 time is now 191\n",
      "working on squaring up HD 1677 time is now 211\n",
      "working on squaring up HD 1970 time is now 278\n",
      "We have addressed underpop in a total of 440 HDs\n",
      "Here is a scatterplot of original (x) to final pop (y)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING UP UNDERPOPPED\n",
    "HDnAddedUnits, HDaddedPop = [0]*nHDs, [0.]*nHDs\n",
    "underPoppedList = list()\n",
    "minDistrictPop, maxDistrictPop = 0.99 * aDP, 1.01 * aDP\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop < minDistrictPop and t in popHDlist:\n",
    "        underPoppedList.append(t)\n",
    "print(\"We will now square up the\",len(underPoppedList),\"HDs with pop <\",int(minDistrictPop),\"to within\",int(maxGap) )\n",
    "startTime = time.time()\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "for ii,t in enumerate(underPoppedList):\n",
    "    if ii%100 == 0:\n",
    "        print(\"working on squaring up HD\",t,\"time is now\",int(time.time() - startTime)) \n",
    "    gap = aDP - HDvPop[t]\n",
    "    origGap = gap\n",
    "    nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "    for u in HDunitList[t]:\n",
    "        for uu in unitNbrs[u]:\n",
    "            if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap:\n",
    "                nearHDlist.append(uu)   #below line: bias toward close, underused\n",
    "                nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  \n",
    "    currList = HDunitList[t].copy()\n",
    "    addedList = list()\n",
    "    stillGoing = True\n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd: \n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else:\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  #bias toward close, underused\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    HDunitList[t] += addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n",
    "    HDaddedPop[t] = np.sum( [unitPop[u] for u in addedList] )\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        print(\"Oops! HD\",t,\"now has overshot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after adding\",HDaddedPop[t] )\n",
    "print(\"We have addressed underpop in a total of\",len(underPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original (x) to final pop (y)\")\n",
    "plt.scatter([HDvPop[t] - HDaddedPop[t] for t in underPoppedList], [HDvPop[t] for t in underPoppedList])\n",
    "plt.axhline(y=aDP, xmin = 0.9*aDP, xmax = 1.1*aDP, ls=\"--\")\n",
    "plt.axvline(x=aDP, ymin = 0.9*aDP, ymax = 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "bc01409d-d9e4-43a6-9fae-c76aa7bdc855",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "previous unit avg and sd usage are 0.96234 0.10715\n",
      "amped unit avg and sd usage are 1.0465 0.16522\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prevUnitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        prevUnitUse[u] += HDweight[t] * nDistricts\n",
    "\n",
    "ampedUnitUse = [0. for u in range(nUnits)]\n",
    "ampedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "ampedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in ampedHDlist[t]:\n",
    "        ampedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "plt.hist(prevUnitUse,bins=50,weights=unitPop,label=\"previous\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.legend()\n",
    "ampedUnitUseAvg, ampedUnitUseSD = getWeightedAvgAndSD(ampedUnitUse,unitPop)\n",
    "print(\"previous unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "f8d72684-2b88-43f7-8509-b77bbfec8db7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final check -- any more disco's ?\n",
      "working on HD 0 time is now 0 sec\n",
      "working on HD 502 time is now 1 sec\n",
      "working on HD 1004 time is now 3 sec\n",
      "working on HD 1506 time is now 5 sec\n",
      "working on HD 2006 time is now 7 sec\n",
      "out of 2151 total HDs, there were 2151 0 0 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 8 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"final check -- any more disco's ?\")\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "6b4cba34-3451-4ffd-a2d9-31a8565a9e72",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs)]\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "dd7d9818-344c-4150-917b-57a4ee051c92",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\n",
      "Let's now square down the 1285 overPopped HDs to within 2992\n",
      "working on squaring down HD 0 time is now 0\n",
      "working on squaring down HD 49 time is now 17\n",
      "working on squaring down HD 83 time is now 19\n",
      "working on squaring down HD 118 time is now 21\n",
      "working on squaring down HD 155 time is now 22\n",
      "working on squaring down HD 188 time is now 24\n",
      "working on squaring down HD 233 time is now 26\n",
      "working on squaring down HD 272 time is now 27\n",
      "working on squaring down HD 314 time is now 29\n",
      "working on squaring down HD 351 time is now 31\n",
      "working on squaring down HD 382 time is now 33\n",
      "working on squaring down HD 422 time is now 34\n",
      "working on squaring down HD 476 time is now 39\n",
      "working on squaring down HD 510 time is now 41\n",
      "working on squaring down HD 544 time is now 44\n",
      "working on squaring down HD 577 time is now 51\n",
      "working on squaring down HD 614 time is now 69\n",
      "working on squaring down HD 669 time is now 71\n",
      "working on squaring down HD 703 time is now 95\n",
      "working on squaring down HD 745 time is now 112\n",
      "working on squaring down HD 778 time is now 153\n",
      "working on squaring down HD 815 time is now 163\n",
      "working on squaring down HD 860 time is now 164\n",
      "working on squaring down HD 1178 time is now 168\n",
      "working on squaring down HD 1228 time is now 184\n",
      "working on squaring down HD 1267 time is now 186\n",
      "working on squaring down HD 1300 time is now 188\n",
      "working on squaring down HD 1341 time is now 190\n",
      "working on squaring down HD 1374 time is now 193\n",
      "working on squaring down HD 1425 time is now 195\n",
      "working on squaring down HD 1461 time is now 197\n",
      "working on squaring down HD 1505 time is now 199\n",
      "working on squaring down HD 1571 time is now 201\n",
      "working on squaring down HD 1625 time is now 203\n",
      "working on squaring down HD 1690 time is now 205\n",
      "working on squaring down HD 1769 time is now 206\n",
      "working on squaring down HD 1816 time is now 208\n",
      "working on squaring down HD 1879 time is now 210\n",
      "working on squaring down HD 1926 time is now 212\n",
      "working on squaring down HD 1964 time is now 216\n",
      "working on squaring down HD 2018 time is now 217\n",
      "working on squaring down HD 2064 time is now 223\n",
      "working on squaring down HD 2124 time is now 226\n",
      "We have addressed overpop in a total of 1285 HDs\n",
      "Here is a scatterplot of original to final pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING DOWN\n",
    "print(\"Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\")\n",
    "#HDunitList = [ampedHDlist[t].copy() for t in range(nHDs)]\n",
    "#HDvPop =     [ampedHDpop[t]         for t in range(nHDs)]\n",
    "#unitUse =    [ampedUnitUse[u]       for u in range(nUnits)] #saving in case I need to re-run\n",
    "HDnShedUnits = [0]*nHDs\n",
    "overPoppedList = list()\n",
    "startTime = time.time()\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop > maxDistrictPop and t in popHDlist:\n",
    "        overPoppedList.append(t)\n",
    "\n",
    "maxGap = 0.9 * np.median(unitPop)   \n",
    "print(\"Let's now square down the\",len(overPoppedList),\"overPopped HDs to within\", int(maxGap))   \n",
    "for ii,t in enumerate(overPoppedList):\n",
    "    if ii%30 == 0:\n",
    "        print(\"working on squaring down HD\",t,\"time is now\",int(time.time()-startTime))\n",
    "    unbroken, noEnclave, sPL, ePL = enclaveCheck(HDunitList[t],unitNbrs)\n",
    "    if not (unbroken and noEnclave):\n",
    "        print(\"SKIPPING shedding attempt on overpopped HD\",t,\"with pop\",HDvPop[t],\"because it was not legal to start\")\n",
    "    else:\n",
    "        #avgDist = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        excess = HDvPop[t] - aDP\n",
    "        origExcess = excess\n",
    "        HDboundaryList, HDboundaryScore = list(), list()  #these will be dynamic lists of the overused border units to jettison\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "        barredSet = set(get2nbrs([starterU],unitNbrs)).union(barredJettisonSet)\n",
    "\n",
    "        for u in set(HDunitList[t]).difference(barredSet):\n",
    "            isBoundary = False\n",
    "            for uu in unitNbrs[u]:\n",
    "                if uu not in HDunitList[t]:\n",
    "                    isBoundary = True\n",
    "                    break\n",
    "            if isBoundary and HDvPop[t] - unitPop[u] > aDP - maxGap:  #shedding this unit won't send us too far under targetpop\n",
    "                HDboundaryList.append(u)\n",
    "                HDboundaryScore.append((1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t])  #low-score = bias toward far, overused\n",
    "        currList = HDunitList[t].copy()\n",
    "        shedList, stillGoing = list(), True\n",
    "        while excess > maxGap and len(HDboundaryList) > 0:   #shed the highest-scoring neighboring overused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(HDboundaryScore), 0, True\n",
    "            while i < len(HDboundaryScore) and notYetPicked:        \n",
    "                listNo = idx[i]   #low (large negative) score is preferred to shed\n",
    "                unitNoToShed = HDboundaryList[listNo]  # attempt to shed this unit ...\n",
    "                tryList = list(set(currList).difference( {unitNoToShed} ) )\n",
    "                unbroken, noEnclave, sPL, ePL = enclaveCheck(tryList,unitNbrs) \n",
    "                if unbroken and noEnclave:             #... if that won't eff up contiguity\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't add any more units without creating an enclave\n",
    "            else: #add this eligible unit\n",
    "                shedList.append(unitNoToShed)\n",
    "                excess -= unitPop[unitNoToShed]        \n",
    "                del currList[currList.index(unitNoToShed) ]\n",
    "                del HDboundaryList[listNo]\n",
    "                del HDboundaryScore[listNo]\n",
    "                for u in unitNbrs[unitNoToShed]:      # ... and ID any neighboring units that will now be on boundary after we shed this unit\n",
    "                    if u in currList and u not in HDboundaryList and (unitPop[u] <= excess + maxGap and\n",
    "                                                                      u not in barredSet): \n",
    "                        HDboundaryList.append(u)\n",
    "                        HDboundaryScore.append( (1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t] )  #low-score, bias toward far & overused       \n",
    "                for uu in HDboundaryList.copy():\n",
    "                    if unitPop[uu] > excess + maxGap:  #checking to see if the latest pop change DQ's any large units on current boundary\n",
    "                        del HDboundaryScore[HDboundaryList.index(uu)]\n",
    "                        del HDboundaryList[HDboundaryList.index(uu)]                \n",
    "        for u in shedList:\n",
    "            unitUse[u] -= HDweight[t] * nDistricts\n",
    "        HDunitList[t], HDvPop[t] = currList.copy(), np.sum( [unitPop[u] for u in currList ] )    \n",
    "        if HDvPop[t] < minDistrictPop:\n",
    "            print(\"Oops! HD\",t,\"now has undershot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after shedding\",\n",
    "                 np.sum([unitPop[u] for u in shedList]) )\n",
    "\n",
    "        HDnShedUnits[t] = -1 * len(shedList)\n",
    "\n",
    "print(\"We have addressed overpop in a total of\",len(overPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original to final pop\")\n",
    "plt.scatter([ampedHDpop[t] for t in overPoppedList], [HDvPop[t] for t in overPoppedList])\n",
    "plt.axhline(aDP, 0.9*aDP, 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "9a19b23e-43cc-4609-9ee7-6d1057a7a9e1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the stats prior to any equi-pop patching\n",
      "orig unit avg and sd usage are 0.96234 0.10715\n",
      "amped unit avg and sd usage are 1.0465 0.16522\n",
      "shed unit avg and sd usage are 0.99997 0.1718\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here are the final populations\n"
     ]
    },
    {
     "data": {
      "image/png": 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AgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWKlVQs7HH3+sH//4x4qLi1Pnzp31/e9/X8XFxc5+Y4wWLFggn8+nyMhIDRkyRMeOHQs4R319vWbOnKn4+HhFRUUpKytLZ8+ebY3pAgAACwU95FRXV+uHP/yhwsLC9F//9V/661//qpdeekm33367U7NkyRItXbpUy5cv16FDh+T1ejV8+HBdvHjRqcnOzlZBQYHy8/O1d+9e1dbWKjMzU5cvXw72lAEAgIVcxhgTzBPOnz9f//M//6M9e/Zcdb8xRj6fT9nZ2Zo3b56kL1dtPB6PFi9erGnTpsnv96tr165av369xo8fL0kqLy9XYmKitm3bphEjRnztPGpqauR2u+X3+xUTExO8BgEAt6xe87e29RRu2EeLRrX1FG5IML+/g76Ss2XLFt1///3613/9VyUkJOjee+/V6tWrnf1lZWWqrKxURkaGMxYREaHBgwerqKhIklRcXKzGxsaAGp/Pp5SUFKemufr6etXU1ARsAADg1hX0kPO3v/1NK1euVHJyst555x09+eSTmjVrln7/+99LkiorKyVJHo8n4DiPx+Psq6ysVHh4uLp06XLNmuby8vLkdrudLTExMditAQCADiToIaepqUn33XefcnNzde+992ratGl6/PHHtXLlyoA6l8sV8NkY02KsuevV5OTkyO/3O9uZM2durhEAANChBT3kdOvWTX369AkY6927t06fPi1J8nq9ktRiRaaqqspZ3fF6vWpoaFB1dfU1a5qLiIhQTExMwAYAAG5dQQ85P/zhD3XixImAsZMnT6pnz56SpKSkJHm9XhUWFjr7GxoatHv3bg0aNEiSlJqaqrCwsICaiooKHT161KkBAAC4ntBgn/AXv/iFBg0apNzcXI0bN04HDx7UqlWrtGrVKklf/poqOztbubm5Sk5OVnJysnJzc9W5c2dNmDBBkuR2uzV16lTNnj1bcXFxio2N1Zw5c9SvXz+lp6cHe8oAAMBCQQ85P/jBD1RQUKCcnBwtXLhQSUlJevnllzVx4kSnZu7cuaqrq9P06dNVXV2ttLQ07dixQ9HR0U7NsmXLFBoaqnHjxqmurk7Dhg3T2rVrFRISEuwpAwAACwX9OTntBc/JAQAEG8/JaX3t+jk5AAAA7QEhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsFKrh5y8vDy5XC5lZ2c7Y8YYLViwQD6fT5GRkRoyZIiOHTsWcFx9fb1mzpyp+Ph4RUVFKSsrS2fPnm3t6QIAAEu0asg5dOiQVq1apXvuuSdgfMmSJVq6dKmWL1+uQ4cOyev1avjw4bp48aJTk52drYKCAuXn52vv3r2qra1VZmamLl++3JpTBgAAlmi1kFNbW6uJEydq9erV6tKlizNujNHLL7+sZ599VmPHjlVKSorWrVunzz//XBs3bpQk+f1+rVmzRi+99JLS09N17733asOGDTpy5Ih27tzZWlMGAAAWabWQ89RTT2nUqFFKT08PGC8rK1NlZaUyMjKcsYiICA0ePFhFRUWSpOLiYjU2NgbU+Hw+paSkODXN1dfXq6amJmADAAC3rtDWOGl+fr5KSkp06NChFvsqKyslSR6PJ2Dc4/Ho1KlTTk14eHjACtCVmivHN5eXl6cXXnghGNMHAAAWCPpKzpkzZ/Tzn/9cGzZsUKdOna5Z53K5Aj4bY1qMNXe9mpycHPn9fmc7c+bMjU8eAABYI+ghp7i4WFVVVUpNTVVoaKhCQ0O1e/duvfLKKwoNDXVWcJqvyFRVVTn7vF6vGhoaVF1dfc2a5iIiIhQTExOwAQCAW1fQQ86wYcN05MgRlZaWOtv999+viRMnqrS0VN/5znfk9XpVWFjoHNPQ0KDdu3dr0KBBkqTU1FSFhYUF1FRUVOjo0aNODQAAwPUE/Zqc6OhopaSkBIxFRUUpLi7OGc/OzlZubq6Sk5OVnJys3Nxcde7cWRMmTJAkud1uTZ06VbNnz1ZcXJxiY2M1Z84c9evXr8WFzAAAAFfTKhcef525c+eqrq5O06dPV3V1tdLS0rRjxw5FR0c7NcuWLVNoaKjGjRunuro6DRs2TGvXrlVISEhbTBkAAHQwLmOMaetJtIaamhq53W75/X6uzwEABEWv+Vvbego37KNFo9p6CjckmN/fvLsKAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYKeghJy8vTz/4wQ8UHR2thIQEjRkzRidOnAioMcZowYIF8vl8ioyM1JAhQ3Ts2LGAmvr6es2cOVPx8fGKiopSVlaWzp49G+zpAgAASwU95OzevVtPPfWU9u/fr8LCQl26dEkZGRn67LPPnJolS5Zo6dKlWr58uQ4dOiSv16vhw4fr4sWLTk12drYKCgqUn5+vvXv3qra2VpmZmbp8+XKwpwwAACzkMsaY1vwDPvnkEyUkJGj37t36p3/6Jxlj5PP5lJ2drXnz5kn6ctXG4/Fo8eLFmjZtmvx+v7p27ar169dr/PjxkqTy8nIlJiZq27ZtGjFixNf+uTU1NXK73fL7/YqJiWnNFgEAt4he87e29RRu2EeLRrX1FG5IML+/W/2aHL/fL0mKjY2VJJWVlamyslIZGRlOTUREhAYPHqyioiJJUnFxsRobGwNqfD6fUlJSnJrm6uvrVVNTE7ABAIBbV6uGHGOMnn76aT3wwANKSUmRJFVWVkqSPB5PQK3H43H2VVZWKjw8XF26dLlmTXN5eXlyu93OlpiYGOx2AABAB9KqIWfGjBn6y1/+ojfffLPFPpfLFfDZGNNirLnr1eTk5Mjv9zvbmTNnvv3EAQBAh9dqIWfmzJnasmWLdu3ape7duzvjXq9XklqsyFRVVTmrO16vVw0NDaqurr5mTXMRERGKiYkJ2AAAwK0r6CHHGKMZM2Zo06ZN+uMf/6ikpKSA/UlJSfJ6vSosLHTGGhoatHv3bg0aNEiSlJqaqrCwsICaiooKHT161KkBAAC4ntBgn/Cpp57Sxo0b9Z//+Z+Kjo52VmzcbrciIyPlcrmUnZ2t3NxcJScnKzk5Wbm5uercubMmTJjg1E6dOlWzZ89WXFycYmNjNWfOHPXr10/p6enBnjIAALBQ0EPOypUrJUlDhgwJGH/99dc1ZcoUSdLcuXNVV1en6dOnq7q6WmlpadqxY4eio6Od+mXLlik0NFTjxo1TXV2dhg0bprVr1yokJCTYUwYAABZq9efktBWekwMACDaek9P6OtRzcgAAANoCIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqhbT0BAACCqdf8rW09BbQTrOQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWCm0rScAAABaT6/5W1vt3B8tGtVq5w4GVnIAAICVCDkAAMBK7T7krFixQklJSerUqZNSU1O1Z8+etp4SAADoANp1yHnrrbeUnZ2tZ599VocPH9aDDz6ohx9+WKdPn27rqQEAgHbOZYwxbT2Ja0lLS9N9992nlStXOmO9e/fWmDFjlJeXd91ja2pq5Ha75ff7FRMT09pTBQDcoNa8IBb/f7TGhcfB/P5ut3dXNTQ0qLi4WPPnzw8Yz8jIUFFRUYv6+vp61dfXO5/9fr+kL/+yAADtT1P95209Bdyk1viOvXLOYKzBtNuQ8+mnn+ry5cvyeDwB4x6PR5WVlS3q8/Ly9MILL7QYT0xMbLU5AgBwK3O/3Hrnvnjxotxu902do92GnCtcLlfAZ2NMizFJysnJ0dNPP+18bmpq0t///nfFxcW1qK+pqVFiYqLOnDlj/a+y6NVO9GqfW6VPiV5tFaxejTG6ePGifD7fTc+p3Yac+Ph4hYSEtFi1qaqqarG6I0kRERGKiIgIGLv99tuv+2fExMRY/y/dFfRqJ3q1z63Sp0SvtgpGrze7gnNFu727Kjw8XKmpqSosLAwYLyws1KBBg9poVgAAoKNotys5kvT0009r0qRJuv/++zVw4ECtWrVKp0+f1pNPPtnWUwMAAO1cuw4548eP1/nz57Vw4UJVVFQoJSVF27ZtU8+ePW/qvBEREXr++edb/HrLRvRqJ3q1z63Sp0SvtmqPvbbr5+QAAAB8W+32mhwAAICbQcgBAABWIuQAAAArEXIAAICV2l3I6dWrl1wuV4vtqaeekiRt2rRJI0aMUHx8vFwul0pLS1uco76+XjNnzlR8fLyioqKUlZWls2fPBtRUV1dr0qRJcrvdcrvdmjRpki5cuBBQc/r0aY0ePVpRUVGKj4/XrFmz1NDQEFBz5MgRDR48WJGRkbrjjju0cOHCb/y+jev12tjYqHnz5qlfv36KioqSz+fTT37yE5WXl1vXqyQtWLBAd999t6KiotSlSxelp6frwIEDVvb6VdOmTZPL5dLLL79sZa9TpkxpsW/AgAFW9ipJ7733nrKysuR2uxUdHa0BAwbo9OnT1vV6tX0ul0u//vWvreu1trZWM2bMUPfu3RUZGanevXsHvDTapl7PnTunKVOmyOfzqXPnzho5cqTef//9Dtmrw7QzVVVVpqKiwtkKCwuNJLNr1y5jjDG///3vzQsvvGBWr15tJJnDhw+3OMeTTz5p7rjjDlNYWGhKSkrM0KFDTf/+/c2lS5ecmpEjR5qUlBRTVFRkioqKTEpKisnMzHT2X7p0yaSkpJihQ4eakpISU1hYaHw+n5kxY4ZT4/f7jcfjMY8++qg5cuSI+cMf/mCio6PNiy++eNO9XrhwwaSnp5u33nrLHD9+3Ozbt8+kpaWZ1NRU63o1xpg33njDFBYWmg8//NAcPXrUTJ061cTExJiqqirrer2ioKDA9O/f3/h8PrNs2bKAfbb0OnnyZDNy5MiAmvPnz1vZ6wcffGBiY2PNM888Y0pKSsyHH35o3n77bXPu3Dnrev3qvoqKCvO73/3OuFwu8+GHH1rX689+9jNz5513ml27dpmysjLz2muvmZCQELN582arem1qajIDBgwwDz74oDl48KA5fvy4eeKJJ0yPHj1MbW1th+v1inYXcpr7+c9/bu68807T1NQUMF5WVnbVkHPhwgUTFhZm8vPznbGPP/7Y3HbbbWb79u3GGGP++te/Gklm//79Ts2+ffuMJHP8+HFjjDHbtm0zt912m/n444+dmjfffNNEREQYv99vjDFmxYoVxu12my+++MKpycvLMz6fr8V8b6bXKw4ePGgkmVOnTlnfq9/vN5LMzp07rez17Nmz5o477jBHjx41PXv2DAg5NvU6efJk88gjj1yz3qZex48fb3784x/fEr0298gjj5iHHnrIyl779u1rFi5cGFBz3333meeee86qXk+cOGEkmaNHjzr7L126ZGJjY83q1as7bK/t7tdVX9XQ0KANGzbopz/96VVfynk1xcXFamxsVEZGhjPm8/mUkpKioqIiSdK+ffvkdruVlpbm1AwYMEButzugJiUlJeAFYSNGjFB9fb2Ki4udmsGDBwc8+GjEiBEqLy/XRx99FPRe/X6/XC6X804uW3ttaGjQqlWr5Ha71b9/f+t6bWpq0qRJk/TMM8+ob9++LY6xqVdJ+tOf/qSEhAR973vf0+OPP66qqirrem1qatLWrVv1ve99TyNGjFBCQoLS0tK0efNm63pt7ty5c9q6daumTp1qZa8PPPCAtmzZoo8//ljGGO3atUsnT57UiBEjrOq1vr5ektSpUyenJiQkROHh4dq7d2+H7bVdh5zNmzfrwoULmjJlyjc+prKyUuHh4erSpUvAuMfjcV72WVlZqYSEhBbHJiQkBNQ0fxFoly5dFB4eft2aK5+bv1j063xdr1988YXmz5+vCRMmOC8+s63Xt99+W//wD/+gTp06admyZSosLFR8fLx1vS5evFihoaGaNWvWVY+xqdeHH35Yb7zxhv74xz/qpZde0qFDh/TQQw85/0O1pdeqqirV1tZq0aJFGjlypHbs2KEf/ehHGjt2rHbv3m1Vr82tW7dO0dHRGjt2rDNmU6+vvPKK+vTpo+7duys8PFwjR47UihUr9MADD1jV6913362ePXsqJydH1dXVamho0KJFi1RZWamKiooO22u7fq3DmjVr9PDDDwfldevGmICfQq72E0kwasz/vSjqm648XXG9XhsbG/Xoo4+qqalJK1as+NpzddRehw4dqtLSUn366adavXq1xo0bpwMHDlz1P5hg9vFNaoLVa3FxsX7zm9+opKTkhs/V0XqVvnw1yxUpKSm6//771bNnT23dujXgS7E1+vgmNcHqtampSZL0yCOP6Be/+IUk6fvf/76Kior029/+VoMHD77muTpar8397ne/08SJEwNWAK6lI/b6yiuvaP/+/dqyZYt69uypd999V9OnT1e3bt2Unp5+zXN1tF7DwsL0hz/8QVOnTlVsbKxCQkKUnp6uhx9++GvP1Z57bbcrOadOndLOnTv1s5/97IaO83q9amhoUHV1dcB4VVWVkwK9Xq/OnTvX4thPPvkkoKZ5WqyurlZjY+N1a64sxTdPoNdzvV4bGxs1btw4lZWVqbCwMOD19bb1GhUVpe9+97saMGCA1qxZo9DQUK1Zs8aqXvfs2aOqqir16NFDoaGhCg0N1alTpzR79mz16tXLql6vplu3burZs6dzx4YtvcbHxys0NFR9+vQJqO3du7dzd5UtvX7Vnj17dOLEiRb7bem1rq5O//Zv/6alS5dq9OjRuueeezRjxgyNHz9eL774olW9SlJqaqpKS0t14cIFVVRUaPv27Tp//rySkpI6bK/t9sLj559/3ni9XtPY2HjV/V934fFbb73ljJWXl1/1wqgDBw44Nfv377/qhVHl5eVOTX5+fosLo26//XZTX1/v1CxatOiGL4y6Vq8NDQ1mzJgxpm/fvgF3GdnY69Xceeed5vnnn7eq108//dQcOXIkYPP5fGbevHnOHG3p9Wo+/fRTExERYdatW2ddrwMHDmxx4fGYMWPMY489Zl2vV0yePLnFHZ829XrlBoht27YF1D7xxBNm+PDhVvV6NSdPnjS33Xabeeeddzpsr+0y5Fy+fNn06NHDzJs3r8W+8+fPm8OHD5utW7caSSY/P98cPnzYVFRUODVPPvmk6d69u9m5c6cpKSkxDz300FVvcbvnnnvMvn37zL59+0y/fv2ueovbsGHDTElJidm5c6fp3r17wC1uFy5cMB6Pxzz22GPmyJEjZtOmTSYmJuaGbnG7Vq+NjY0mKyvLdO/e3ZSWlgbc9vfVf+g29FpbW2tycnLMvn37zEcffWSKi4vN1KlTTURERMCV/jb0ejXN766ypdeLFy+a2bNnm6KiIlNWVmZ27dplBg4caO644w5TU1NjVa/GGLNp0yYTFhZmVq1aZd5//33z6quvmpCQELNnzx7rejXmywDQuXNns3Llyqvut6XXwYMHm759+5pdu3aZv/3tb+b11183nTp1MitWrLCu1//4j/8wu3btMh9++KHZvHmz6dmzpxk7dmxATUfq1Zh2GnLeeecdI8mcOHGixb7XX3/dSGqxXfmJ3xhj6urqzIwZM0xsbKyJjIw0mZmZ5vTp0wHnOX/+vJk4caKJjo420dHRZuLEiaa6ujqg5tSpU2bUqFEmMjLSxMbGmhkzZgTczmaMMX/5y1/Mgw8+aCIiIozX6zULFiy4oZR5rV6vrFRdbfvq81Zs6LWurs786Ec/Mj6fz4SHh5tu3bqZrKwsc/DgwRZ1Hb3Xq7layLGh188//9xkZGSYrl27mrCwMNOjRw8zefLkFn3Y0OsVa9asMd/97ndNp06dTP/+/QOepWJbr6+99pqJjIw0Fy5cuOp+W3qtqKgwU6ZMMT6fz3Tq1Mncdddd5qWXXgo4vy29/uY3vzHdu3d3/nt97rnnAn6o7mi9GmOMy5gbfXwgAABA+9duLzwGAAC4GYQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFjp/wDEf6YD0HqrNwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 2157\n",
      "working on HD 301 out of 2157\n",
      "working on HD 602 out of 2157\n",
      "working on HD 903 out of 2157\n",
      "working on HD 1204 out of 2157\n",
      "working on HD 1506 out of 2157\n",
      "working on HD 1806 out of 2157\n",
      "working on HD 2106 out of 2157\n",
      "all done checking HD and complement contiguity for all 2157 HDs\n",
      "underpop contigFail, enclaveFail = 0 0 out of 440\n",
      "overpop contigFail, enclaveFail = 0 0 out of 1285\n",
      "total contigFail, enclaveFail =  0 0\n",
      "CCB unit 1858 with pop 263851.0 now has use 0.98622\n"
     ]
    }
   ],
   "source": [
    "print(\"Here are the stats prior to any equi-pop patching\")\n",
    "shedUnitUse = [0. for u in range(nUnits)]\n",
    "shedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "shedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in shedHDlist[t]:\n",
    "        shedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "plt.hist(latestUnitUse,bins=50,weights=unitPop,label=\"pre-squaring\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.hist(shedUnitUse, bins=50, weights=unitPop,label=\"shed\",histtype=\"step\")\n",
    "plt.legend()\n",
    "shedUnitUseAvg, shedUnitUseSD = getWeightedAvgAndSD(shedUnitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "print(\"shed unit avg and sd usage are\", r5(shedUnitUseAvg),  r5(shedUnitUseSD) )\n",
    "plt.show()\n",
    "# note: when I ran this early Jan, went from 0.12662 to 0.09961 to 0.09432 SD orig-amped-shed, but contig not yet done\n",
    "print(\"and here are the final populations\")\n",
    "plt.hist([shedHDpop[t] for t in popHDlist],bins=20)\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "failEnclaveSet, failContigSet = set(), set()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs,5)\n",
    "    if not noEnclave:\n",
    "        noEnclave, eList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if not unbroken or not noEnclave:\n",
    "        #print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "        pass\n",
    "    if not unbroken:\n",
    "        failContigSet.add(t)\n",
    "    if not noEnclave:\n",
    "        failEnclaveSet.add(t)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")\n",
    "failContigUnderpopped, failEnclaveUnderpopped = set(underPoppedList).intersection(failContigSet), set(underPoppedList).intersection(failEnclaveSet)\n",
    "failContigOverpopped, failEnclaveOverpopped = set(overPoppedList).intersection(failContigSet), set(overPoppedList).intersection(failEnclaveSet)\n",
    "print(\"underpop contigFail, enclaveFail =\",len(failContigUnderpopped), len(failEnclaveUnderpopped),\"out of\",len(underPoppedList))\n",
    "print(\"overpop contigFail, enclaveFail =\",len(failContigOverpopped), len(failEnclaveOverpopped),\"out of\",len(overPoppedList))\n",
    "print(\"total contigFail, enclaveFail = \",len(failContigSet), len(failEnclaveSet) )\n",
    "for u in range(nUnits):\n",
    "    if abs( allUnits[u]%1 - 0.25) < 0.01:\n",
    "        print(\"CCB unit\",u,\"with pop\",unitPop[u],\"now has use\",r5(unitUse[u]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "e7bfa674-8fc8-42c8-86e9-76d334d3c47c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"unpatchedContigGoodPop.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "c0c3a3d8-cf79-40ad-aa2e-b6793266f1d4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to patching, write these contiguous lists to a file, converting to vtd lists\n"
     ]
    }
   ],
   "source": [
    "#SKIP THIS FOR STATES WITH FRAGMENTED VTD'S ###########\n",
    "print(\"prior to patching, write these contiguous lists to a file, converting to vtd lists\")\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        for i,uu in enumerate(surrounders):\n",
    "            if u == uu:\n",
    "                unitVTDlist[u].append(surroundedVTDs[i])\n",
    "\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        vtdList[t] += unitVTDlist[u]\n",
    "    #add more stuff here to convert to vtd lists\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"needsEnclaveRemoval.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "1b794580-5a58-4238-b310-7c87872ed679",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this optional RESTART block pulls in an existing UNIT (not vtd) list for patching\n",
      "Must have already established unitGeoms, pops, topology\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched UNIT list, e.g. ./2024state_HD_output/FL7211unpatchedContigGoodPop.csv ./2024state_HD_output/MA2157unpatchedContigGoodPop.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I read in 2157 HD lists of units\n",
      "orig unit avg and sd usage are 0.99997 0.1718\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"this optional RESTART block pulls in an existing UNIT (not vtd) list for patching\") #vtdlist for patching\")\n",
    "print(\"Must have already established unitGeoms, pops, topology\")\n",
    "infile = input(\"enter the unpatched UNIT list, e.g. ./2024state_HD_output/FL7211unpatchedContigGoodPop.csv\")\n",
    "inDF = pd.read_csv(infile)\n",
    "vtdListString = inDF[\"HDunitList\"]  #inDF[\"HDvtdList\"]\n",
    "nHDs = len(vtdListString)\n",
    "inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "HDweight = inDF[\"HDweight\"]\n",
    "HDvPop = inDF[\"HDvPop\"].to_list()\n",
    "hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "print(\"I read in\",nHDs,\"HD lists of units\") #vtds\")\n",
    "HDunitList = [inVTDlist[t].copy() for t in range(nHDs)]\n",
    "#HDunitList = [list() for t in range(nHDs)]\n",
    "#for t in range(nHDs):\n",
    "#    if t%500 == 0:\n",
    "#        print(\"working on converting HD\",t,\"from vtd list to unit list\")\n",
    "#    for v in inVTDlist[t]:  #this will skip over surrounded units, whose pops we have added to their surrounders\n",
    "#        if v in allUnits:\n",
    "#            HDunitList[t].append(allUnits.index(v))\n",
    "#    for c in unitCounties:\n",
    "#        if countyTractList[c][0] in inVTDlist[t]:\n",
    "#            u = allUnits.index(c+0.5)\n",
    "#            HDunitList[t].append(u)\n",
    "#    for j, L in enumerate(CCBlist):\n",
    "#        c = L[0]\n",
    "#        if countyTractList[c][0] in inVTDlist[t]:\n",
    "#            u = allUnits.index(j+0.25)\n",
    "#            HDunitList[t].append(u) \n",
    "            \n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "5f3a5392-11f1-4061-a458-aecd834b2748",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on HD 0\n",
      "working on HD 800\n",
      "working on HD 1600\n",
      "all avgDist to HD centers computed\n"
     ]
    }
   ],
   "source": [
    "#needed for restart only  about 5sec per 2000 HDs\n",
    "avgDist = [0. for t in range(nHDs)]\n",
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if t%800 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    if HDvPop[t] > 0.1 * aDP:\n",
    "        avgDist[t] = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        popHDlist.append(t)\n",
    "print(\"all avgDist to HD centers computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "ea0db02f-9df8-4906-acb4-086c29c0fcc1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on HD 0\n",
      "working on HD 300\n",
      "working on HD 600\n",
      "working on HD 900\n",
      "working on HD 1200\n",
      "working on HD 1500\n",
      "working on HD 1800\n",
      "working on HD 2100\n",
      "out of 2151 total HDs, there were 2151 0 0 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n"
     ]
    }
   ],
   "source": [
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()  #optional contiguity check\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "27b42f93-dc87-4afe-8eb3-f30fdff4a737",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking unitUse, pop for county clusters\n",
      "1858 0.98622 263851.0\n"
     ]
    }
   ],
   "source": [
    "print(\"checking unitUse, pop for county clusters\")\n",
    "for uNo in allUnits:\n",
    "    if uNo - int(uNo) > 0.23 and uNo - int(uNo) < 0.27:\n",
    "        u = allUnits.index(uNo)\n",
    "        print(u,r5(unitUse[u]), unitPop[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "72941b8f-7be6-4a31-9377-618f44a34336",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unpatchedUnitUse = unitUse.copy()              #... for safekeeping\n",
    "unpatchedHDlist =  [HDunitList[t].copy() for t in range(nHDs) ]\n",
    "unpatchedHDpop =   HDvPop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "5f9ca091-1eb6-4135-a233-7f5ae0f5e3d1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unitUse = unpatchedUnitUse.copy()              #... for restarting\n",
    "HDunitList =  [unpatchedHDlist[t].copy() for t in range(nHDs) ]\n",
    "HDvPop =   unpatchedHDpop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "e9dd457d-fb37-49cf-9b19-19c17c328ede",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we exchange in under-used, THEN shed over-used\n",
      "Currently, we will stop patching when the overall sd of usage is less than 0.05\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated maxSD value 0.05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will stop patching on individual underused units when their usage exceeds 0.95\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated stopMinUse value 0.97\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 429 units out of 1859 with usage below 0.97\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.03\n",
      "enter 1 to print out stats for every patched HD, otherwise enter reporting frequency 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's further tighten the distro with exchanges up to 23443\n",
      "current avg and SD of unit usage are 1.00004 0.07968 . Now trying to increase up to 18 units' underusage\n",
      "all done trying to reduce usage of unit 462 final usage = 0.97102 5 sec elapsed 54 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00009 0.07855\n",
      "all done trying to reduce usage of unit 530 final usage = 0.95437 11 sec elapsed 49 36 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00011 0.07744\n",
      "all done trying to reduce usage of unit 497 final usage = 0.79714 14 sec elapsed 11 3 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.00011 0.07719\n",
      "all done trying to reduce usage of unit 461 final usage = 0.80166 18 sec elapsed 17 6 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00012 0.07667\n",
      "all done trying to reduce usage of unit 493 final usage = 0.90365 26 sec elapsed 35 23 successful, failed patches, couldn't start= 18\n",
      "current avg and SD of usage are 1.00011 0.07585\n",
      "all done trying to reduce usage of unit 538 final usage = 0.83298 31 sec elapsed 18 44 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00011 0.07538\n",
      "all done trying to reduce usage of unit 531 final usage = 0.83264 36 sec elapsed 19 22 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00011 0.07497\n",
      "all done trying to reduce usage of unit 506 final usage = 0.92684 43 sec elapsed 42 11 successful, failed patches, couldn't start= 31\n",
      "current avg and SD of usage are 1.00012 0.07406\n",
      "all done trying to reduce usage of unit 334 final usage = 0.85079 54 sec elapsed 24 0 successful, failed patches, couldn't start= 61\n",
      "current avg and SD of usage are 1.00013 0.07374\n",
      "starting to increase usage for unit 401 with usage 0.76921 . Total UU units tried = 10\n",
      "all done trying to reduce usage of unit 401 final usage = 0.87553 65 sec elapsed 39 0 successful, failed patches, couldn't start= 34\n",
      "current avg and SD of usage are 1.00014 0.07331\n",
      "all done trying to reduce usage of unit 535 final usage = 0.78975 73 sec elapsed 14 72 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00015 0.07301\n",
      "all done trying to reduce usage of unit 480 final usage = 0.81285 81 sec elapsed 10 21 successful, failed patches, couldn't start= 39\n",
      "current avg and SD of usage are 1.00015 0.07276\n",
      "all done trying to reduce usage of unit 449 final usage = 0.82302 89 sec elapsed 11 54 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00017 0.0725\n",
      "all done trying to reduce usage of unit 404 final usage = 0.89918 101 sec elapsed 37 1 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 1.00018 0.07208\n",
      "all done trying to reduce usage of unit 411 final usage = 0.86686 110 sec elapsed 27 21 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00018 0.07141\n",
      "all done trying to reduce usage of unit 325 final usage = 0.81851 119 sec elapsed 11 25 successful, failed patches, couldn't start= 47\n",
      "current avg and SD of usage are 1.00019 0.07117\n",
      "all done trying to reduce usage of unit 558 final usage = 0.81402 123 sec elapsed 9 15 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 1.00019 0.07089\n",
      "all done trying to reduce usage of unit 498 final usage = 0.89165 129 sec elapsed 27 20 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.0002 0.07038\n"
     ]
    }
   ],
   "source": [
    "#FIRST PATCHING BLOCK - boosting underuse.  Suppresses long strings.  For some states (e.g. MA), do overpopped first. MA:over-und-over-und\n",
    "print(\"Here, we exchange in under-used, THEN shed over-used\")\n",
    "maxSD = 0.05  #0.07  #0.05   #adjust down if distro already tight\n",
    "print(\"Currently, we will stop patching when the overall sd of usage is less than\",maxSD)\n",
    "maxSD = float(input(\"enter updated maxSD value\"))\n",
    "stopMinUse = 1. - maxSD\n",
    "print(\"we will stop patching on individual underused units when their usage exceeds\",r5(stopMinUse))\n",
    "stopMinUse = float(input(\"enter updated stopMinUse value\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage below\",stopMinUse)\n",
    "nSmallUsers = 5\n",
    "maxExchangePop = 0.05*aDP \n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP\n",
    "debug1 = int(input(\"enter 1 to print out stats for every patched HD, otherwise enter reporting frequency\"))\n",
    "print(\"Let's further tighten the distro with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "maxNtries = int(currSD * nUnits / nDistricts)   #try up to about 10% of the avg no of units per district\n",
    "attemptedSmallUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to increase up to\",maxNtries,\"units' underusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedSmallUs) < maxNtries: \n",
    "    #each round, find the (5) most underused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nSmallFound, smallUsers = 0,0,list()\n",
    "    while nSmallFound < nSmallUsers:\n",
    "        consideredSmallU = idx[idxNo]\n",
    "        if consideredSmallU not in attemptedSmallUs:\n",
    "            smallUsers.append(consideredSmallU)\n",
    "            nSmallFound +=1\n",
    "        idxNo +=1\n",
    "    UUUclusters = [ [b] + unitNbrs[b] for b in smallUsers ]\n",
    "    smallUnderUse = [np.sum([(unitUse[j] - 1.) for j in UUUclusters[i] ]) for i in range(nSmallUsers) ]\n",
    "    smallI = smallUnderUse.index(np.max(smallUnderUse))\n",
    "    UUU = smallUsers[ smallI ]  #pick the unit that centers cluster with least composite use\n",
    "    attemptedSmallUs.append(UUU)  #so we don't try this unit again in a future loop\n",
    "    UUUc = UUUclusters[smallI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    if len(attemptedSmallUs) % debug1 == 0:\n",
    "        print(\"starting to increase usage for unit\",UUU,\"with usage\",r5(unitUse[UUU]),\". Total UU units tried =\",len(attemptedSmallUs) )\n",
    "    for u in UUUc.copy():\n",
    "        if unitUse[u] > 1.:\n",
    "            UUUc.remove(u)  #...drop any overused neighbors of the primary UUU from the target sheddable cluster\n",
    "    uuuHDs, uuuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if UUU not in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(UUUc) ) > 0: #the UUU or one of its underused 1-neighbors adjoins this HD\n",
    "                uuuHDs.append(t)  #Note: we'll check later if we can contiguously pick up the UUU's cluster\n",
    "                uuuDists.append( unitCP[UUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    idx0 = np.argsort(uuuDists)\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo = 0,0,0,-1\n",
    "    while unitUse[UUU] < stopMinUse and idxNo < 0.8*len(uuuHDs): #arbitrarily only examine 80% closest of HDs\n",
    "        excess, giveUpOnShedding = 99*aDP, True  #default = failed swap\n",
    "        idxNo += 1\n",
    "        if idxNo % 20 == 0 and debug1 == 1:\n",
    "            print(\"try to add unit\",UUU,\"from\",abs(idxNo),\"th HD.  Usage up to\",unitUse[UUU] )\n",
    "        t = uuuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).union(set(UUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        loopUUUc = UUUc.copy()  #default; we will add whole cluster\n",
    "        if not (contig and complementContig): #we can't add whole cluster; neighbors may be enclavy.   Try just adding the UUU\n",
    "            trySet = set(HDunitList[t]).union({UUU})\n",
    "            contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "            loopUUUc = [UUU]\n",
    "        if contig and complementContig:  #we can at least add the cluster, let's go for more\n",
    "            HDuuuSet = set(loopUUUc).difference(set(HDunitList[t]))  #the subset of the UUU cluster that adjoins (NOT in) this HD\n",
    "            HDuuuCpop = np.sum([unitPop[u] for u in HDuuuSet])\n",
    "            giveUpOnAdding = False            \n",
    "            addCandidates, addUseDists = list(), list()\n",
    "            uuCandidates = getAdjoiners(trySet, unitNbrs)  #any adjoiner can be picked up, even if far from UUUc\n",
    "            for u in uuCandidates:\n",
    "                if HDuuuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    addCandidates.append(u)  #Below's relative scoring of use and distance is a bit arbitrary\n",
    "                    addUseDists.append((unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t])  #bias toward close, underused\n",
    "            if len(addCandidates) == 0:\n",
    "                giveUpOnAdding = True\n",
    "            while HDuuuCpop < maxExchangePop and not giveUpOnAdding:\n",
    "                addNneighbors = [len( set(unitNbrs[addC]).intersection(trySet) ) for addC in addCandidates ]\n",
    "                addScores = addUseDists.copy()\n",
    "                for jjj, u in enumerate(addCandidates):\n",
    "                    if addNneighbors[jjj] == 1:\n",
    "                        addScores[jjj] += 0.4321    #discourage growing fingers\n",
    "                #print(\"HDuuuCpop is now\",HDuuuCpop)\n",
    "                idx, ij, notYetPicked = np.argsort(addScores), 0, True\n",
    "                while ij < 0.5*len(addScores) and notYetPicked:\n",
    "                    listNo = idx[ij]   #work from low to high score\n",
    "                    addU = addCandidates[listNo]\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.union({addU}) ), unitNbrs)\n",
    "                    if contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDuuuCpop += unitPop[addU]\n",
    "                        HDuuuSet.add(addU)\n",
    "                        trySet.add(addU)\n",
    "                        del addUseDists[addCandidates.index(addU)]\n",
    "                        del addCandidates[addCandidates.index(addU)]                        \n",
    "                        for uu in list(set(unitNbrs[addU]).difference(trySet) ): \n",
    "                            if uu not in addCandidates and unitPop[uu] + HDuuuCpop < maxExchangePop and unitUse[uu] < 1.01:\n",
    "                                addCandidates.append(uu)\n",
    "                                addUseDists.append( (unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnAdding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            addSet = HDuuuSet.copy()  #we're done building the list of underused units to add to this HD\n",
    "            trySet = set(HDunitList[t]).union(addSet) \n",
    "            excess = np.sum([unitPop[u] for u in trySet]) - aDP\n",
    "            shedCandidates, shedScores, giveUpOnShedding = list(), list(), False\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if unitPop[u] <= excess + maxGap:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward OVERUSED, far\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            shedSet = set()\n",
    "            while excess > maxGap and not giveUpOnShedding:\n",
    "                #print(\"excess pop is now\",excess,\"for HD\",t)\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    if excess - unitPop[shedU] >= -1*maxGap:\n",
    "                        contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                        if contig and cContig:\n",
    "                            notYetPicked = False\n",
    "                            excess -= unitPop[shedU]\n",
    "                            trySet.remove(shedU)\n",
    "                            shedSet.add(shedU)\n",
    "                            del shedScores[shedCandidates.index(shedU)]\n",
    "                            del shedCandidates[shedCandidates.index(shedU)]\n",
    "                            newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                            for u in newSheddables:\n",
    "                                if excess - unitPop[u] >= -1*maxGap and u not in shedCandidates:\n",
    "                                    shedCandidates.append(u)  #we'll check enclavity if ever picked\n",
    "                                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                            for kkk, u in enumerate(shedCandidates): #checking if this shed eliminates high-pop future sheds ...\n",
    "                                if excess - unitPop[u] < -1*maxGap:\n",
    "                                    del shedScores[kkk]\n",
    "                                    del shedCandidates[kkk]\n",
    "                    ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all shedcandidates would create a discontig, so can't shed enough units to square pop\n",
    "            legitSwap = False\n",
    "            if abs(excess) <= 2.*maxGap:  #giving ourselves a bit more margin after exchanges\n",
    "                contig,cContig, __, ___ = enclaveCheck(list(trySet), unitNbrs) \n",
    "                if contig and cContig:\n",
    "                    legitSwap = True\n",
    "            if legitSwap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t] = np.sum([ unitPop[u] for u in HDunitList[t] ])\n",
    "                nPatchSuccess +=1\n",
    "                #print(\"we added\",addSet,\"and shed units\",shedSet,\"from HD\",t)\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "        else:  #adding neither the UUU cluster or just the UUU worked; couldn't even start\n",
    "            nCouldntStart +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "\n",
    "    print(\"all done trying to reduce usage of unit\",UUU,\"final usage =\",r5(unitUse[UUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "861f29d3-b727-49af-90a8-aa9a963e0741",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 0.99997 1.0002 and their SDs are 0.1718 0.07038\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 2157\n",
      "working on HD 300 out of 2157\n",
      "working on HD 600 out of 2157\n",
      "working on HD 900 out of 2157\n",
      "working on HD 1200 out of 2157\n",
      "working on HD 1500 out of 2157\n",
      "working on HD 1800 out of 2157\n",
      "working on HD 2100 out of 2157\n",
      "all done checking HD and complement contiguity for all 2157 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "77c07848-50c2-4fe9-8841-768b02bf4ac4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default use threshold for moving on to next overused unit is 1.12\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated stopMaxUse value 1.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 581 units out of 1859 with usage above 1.02\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.03\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this block reduces overuse, with exchanges up to 23443\n",
      "current avg and SD of unit usage are 1.0002 0.07038 . Now trying to reduce up to 581 units' overusage\n",
      "starting to reduce usage for unit 1540 with usage 1.14832 . Total units tried = 1\n",
      "try to drop unit 1540 from 13 th HD.  usage down to 1.1420265701571102\n",
      "try to drop unit 1540 from 26 th HD.  usage down to 1.1420265701571102\n",
      "try to drop unit 1540 from 39 th HD.  usage down to 1.1420265701571102\n",
      "try to drop unit 1540 from 52 th HD.  usage down to 1.1420265701571102\n",
      "try to drop unit 1540 from 65 th HD.  usage down to 1.138029936910943\n",
      "all done trying to reduce usage of unit 1540 final usage = 1.13803 0 sec elapsed 3 26 successful, failed patches, couldn't start= 37\n",
      "current avg and SD of usage are 1.0002 0.07031\n",
      "starting to reduce usage for unit 1535 with usage 1.14415 . Total units tried = 2\n",
      "try to drop unit 1535 from 11 th HD.  usage down to 1.1441466520869554\n",
      "try to drop unit 1535 from 22 th HD.  usage down to 1.1441466520869554\n",
      "try to drop unit 1535 from 33 th HD.  usage down to 1.1441466520869554\n",
      "try to drop unit 1535 from 44 th HD.  usage down to 1.1403070619467026\n",
      "try to drop unit 1535 from 55 th HD.  usage down to 1.1320349870416937\n",
      "all done trying to reduce usage of unit 1535 final usage = 1.12309 2 sec elapsed 5 35 successful, failed patches, couldn't start= 18\n",
      "current avg and SD of usage are 1.0002 0.07016\n",
      "starting to reduce usage for unit 1261 with usage 1.12877 . Total units tried = 3\n",
      "try to drop unit 1261 from 14 th HD.  usage down to 1.1287700836296048\n",
      "try to drop unit 1261 from 28 th HD.  usage down to 1.1287700836296048\n",
      "try to drop unit 1261 from 42 th HD.  usage down to 1.1287700836296048\n",
      "try to drop unit 1261 from 56 th HD.  usage down to 1.1287700836296048\n",
      "try to drop unit 1261 from 70 th HD.  usage down to 1.1287700836296048\n",
      "all done trying to reduce usage of unit 1261 final usage = 1.12877 3 sec elapsed 0 32 successful, failed patches, couldn't start= 41\n",
      "current avg and SD of usage are 1.0002 0.07016\n",
      "starting to reduce usage for unit 1583 with usage 1.12898 . Total units tried = 4\n",
      "try to drop unit 1583 from 15 th HD.  usage down to 1.1289828884180562\n",
      "try to drop unit 1583 from 30 th HD.  usage down to 1.1289828884180562\n",
      "try to drop unit 1583 from 45 th HD.  usage down to 1.125670189278081\n",
      "try to drop unit 1583 from 60 th HD.  usage down to 1.0899030529093336\n",
      "try to drop unit 1583 from 75 th HD.  usage down to 1.0742624699550842\n",
      "all done trying to reduce usage of unit 1583 final usage = 1.07119 5 sec elapsed 16 55 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00019 0.0699\n",
      "starting to reduce usage for unit 1295 with usage 1.13315 . Total units tried = 5\n",
      "try to drop unit 1295 from 14 th HD.  usage down to 1.13315249667943\n",
      "try to drop unit 1295 from 28 th HD.  usage down to 1.13315249667943\n",
      "try to drop unit 1295 from 42 th HD.  usage down to 1.13315249667943\n",
      "try to drop unit 1295 from 56 th HD.  usage down to 1.13315249667943\n",
      "try to drop unit 1295 from 70 th HD.  usage down to 1.13315249667943\n",
      "all done trying to reduce usage of unit 1295 final usage = 1.13315 7 sec elapsed 0 46 successful, failed patches, couldn't start= 26\n",
      "current avg and SD of usage are 1.00019 0.0699\n",
      "starting to reduce usage for unit 1321 with usage 1.12513 . Total units tried = 6\n",
      "try to drop unit 1321 from 14 th HD.  usage down to 1.1251296423555486\n",
      "try to drop unit 1321 from 28 th HD.  usage down to 1.1251296423555486\n",
      "try to drop unit 1321 from 42 th HD.  usage down to 1.1251296423555486\n",
      "try to drop unit 1321 from 56 th HD.  usage down to 1.1212900522152958\n",
      "try to drop unit 1321 from 70 th HD.  usage down to 1.1123112833336726\n",
      "all done trying to reduce usage of unit 1321 final usage = 1.10843 9 sec elapsed 4 56 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 1.00019 0.0698\n",
      "starting to reduce usage for unit 1266 with usage 1.12296 . Total units tried = 7\n",
      "try to drop unit 1266 from 17 th HD.  usage down to 1.1229617646978214\n",
      "try to drop unit 1266 from 34 th HD.  usage down to 1.1229617646978214\n",
      "try to drop unit 1266 from 51 th HD.  usage down to 1.1229617646978214\n",
      "try to drop unit 1266 from 68 th HD.  usage down to 1.1204251771392486\n",
      "try to drop unit 1266 from 85 th HD.  usage down to 1.1003020377053143\n",
      "all done trying to reduce usage of unit 1266 final usage = 1.09631 11 sec elapsed 7 39 successful, failed patches, couldn't start= 42\n",
      "current avg and SD of usage are 1.00019 0.06965\n",
      "starting to reduce usage for unit 1331 with usage 1.12223 . Total units tried = 8\n",
      "try to drop unit 1331 from 11 th HD.  usage down to 1.1222345868378205\n",
      "try to drop unit 1331 from 22 th HD.  usage down to 1.1148785967173118\n",
      "try to drop unit 1331 from 33 th HD.  usage down to 1.1148785967173118\n",
      "try to drop unit 1331 from 44 th HD.  usage down to 1.111039006577059\n",
      "try to drop unit 1331 from 55 th HD.  usage down to 1.111039006577059\n",
      "all done trying to reduce usage of unit 1331 final usage = 1.11104 12 sec elapsed 4 51 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00019 0.06958\n",
      "starting to reduce usage for unit 1333 with usage 1.1216 . Total units tried = 9\n",
      "try to drop unit 1333 from 12 th HD.  usage down to 1.1215973104659975\n",
      "try to drop unit 1333 from 24 th HD.  usage down to 1.1181514660840512\n",
      "try to drop unit 1333 from 36 th HD.  usage down to 1.1181514660840512\n",
      "try to drop unit 1333 from 48 th HD.  usage down to 1.1181514660840512\n",
      "try to drop unit 1333 from 60 th HD.  usage down to 1.1143118759437984\n",
      "all done trying to reduce usage of unit 1333 final usage = 1.11431 15 sec elapsed 3 52 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00019 0.06954\n",
      "starting to reduce usage for unit 1538 with usage 1.12176 . Total units tried = 10\n",
      "try to drop unit 1538 from 13 th HD.  usage down to 1.1217600435395159\n",
      "try to drop unit 1538 from 26 th HD.  usage down to 1.1217600435395159\n",
      "try to drop unit 1538 from 39 th HD.  usage down to 1.1217600435395159\n",
      "try to drop unit 1538 from 52 th HD.  usage down to 1.1174993957964487\n",
      "try to drop unit 1538 from 65 th HD.  usage down to 1.1174993957964487\n",
      "all done trying to reduce usage of unit 1538 final usage = 1.1175 16 sec elapsed 1 33 successful, failed patches, couldn't start= 32\n",
      "current avg and SD of usage are 1.00019 0.06952\n",
      "starting to reduce usage for unit 1593 with usage 1.12084 . Total units tried = 11\n",
      "try to drop unit 1593 from 19 th HD.  usage down to 1.1208428207614987\n",
      "try to drop unit 1593 from 38 th HD.  usage down to 1.1208428207614987\n",
      "try to drop unit 1593 from 57 th HD.  usage down to 1.1208428207614987\n",
      "try to drop unit 1593 from 76 th HD.  usage down to 1.1159357926984372\n",
      "try to drop unit 1593 from 95 th HD.  usage down to 1.1057951324317525\n",
      "all done trying to reduce usage of unit 1593 final usage = 1.10013 21 sec elapsed 7 88 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00018 0.06941\n",
      "starting to reduce usage for unit 855 with usage 1.11901 . Total units tried = 12\n",
      "try to drop unit 855 from 13 th HD.  usage down to 1.1190140651730576\n",
      "try to drop unit 855 from 26 th HD.  usage down to 1.1190140651730576\n",
      "try to drop unit 855 from 39 th HD.  usage down to 1.1190140651730576\n",
      "try to drop unit 855 from 52 th HD.  usage down to 1.1190140651730576\n",
      "try to drop unit 855 from 65 th HD.  usage down to 1.1190140651730576\n",
      "all done trying to reduce usage of unit 855 final usage = 1.11158 23 sec elapsed 2 44 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 1.00018 0.06937\n",
      "starting to reduce usage for unit 1539 with usage 1.11879 . Total units tried = 13\n",
      "try to drop unit 1539 from 11 th HD.  usage down to 1.118788742455876\n",
      "try to drop unit 1539 from 22 th HD.  usage down to 1.118788742455876\n",
      "try to drop unit 1539 from 33 th HD.  usage down to 1.118788742455876\n",
      "try to drop unit 1539 from 44 th HD.  usage down to 1.118788742455876\n",
      "try to drop unit 1539 from 55 th HD.  usage down to 1.118788742455876\n",
      "all done trying to reduce usage of unit 1539 final usage = 1.11879 24 sec elapsed 0 53 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00018 0.06937\n",
      "starting to reduce usage for unit 1320 with usage 1.118 . Total units tried = 14\n",
      "try to drop unit 1320 from 12 th HD.  usage down to 1.11546238739365\n",
      "try to drop unit 1320 from 24 th HD.  usage down to 1.11546238739365\n",
      "try to drop unit 1320 from 36 th HD.  usage down to 1.11546238739365\n",
      "try to drop unit 1320 from 48 th HD.  usage down to 1.11546238739365\n",
      "try to drop unit 1320 from 60 th HD.  usage down to 1.1062446398725925\n",
      "all done trying to reduce usage of unit 1320 final usage = 1.10624 26 sec elapsed 3 54 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00018 0.06931\n",
      "starting to reduce usage for unit 1409 with usage 1.11723 . Total units tried = 15\n",
      "try to drop unit 1409 from 9 th HD.  usage down to 1.1172251393578616\n",
      "try to drop unit 1409 from 18 th HD.  usage down to 1.1172251393578616\n",
      "try to drop unit 1409 from 27 th HD.  usage down to 1.1172251393578616\n",
      "try to drop unit 1409 from 36 th HD.  usage down to 1.1172251393578616\n",
      "try to drop unit 1409 from 45 th HD.  usage down to 1.1172251393578616\n",
      "all done trying to reduce usage of unit 1409 final usage = 1.11339 28 sec elapsed 1 39 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00018 0.06929\n",
      "starting to reduce usage for unit 1516 with usage 1.11686 . Total units tried = 16\n",
      "try to drop unit 1516 from 11 th HD.  usage down to 1.1168598434375787\n",
      "try to drop unit 1516 from 22 th HD.  usage down to 1.1168598434375787\n",
      "try to drop unit 1516 from 33 th HD.  usage down to 1.1168598434375787\n",
      "try to drop unit 1516 from 44 th HD.  usage down to 1.1032608208602155\n",
      "try to drop unit 1516 from 55 th HD.  usage down to 1.0994667504608076\n",
      "all done trying to reduce usage of unit 1516 final usage = 1.09563 32 sec elapsed 5 36 successful, failed patches, couldn't start= 17\n",
      "current avg and SD of usage are 1.00019 0.06919\n",
      "starting to reduce usage for unit 1232 with usage 1.11964 . Total units tried = 17\n",
      "try to drop unit 1232 from 13 th HD.  usage down to 1.1196410996031962\n",
      "try to drop unit 1232 from 26 th HD.  usage down to 1.1196410996031962\n",
      "try to drop unit 1232 from 39 th HD.  usage down to 1.1171045120446235\n",
      "try to drop unit 1232 from 52 th HD.  usage down to 1.1035282493376828\n",
      "try to drop unit 1232 from 65 th HD.  usage down to 1.0814727969048907\n",
      "all done trying to reduce usage of unit 1232 final usage = 1.06485 37 sec elapsed 15 46 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00018 0.06892\n",
      "starting to reduce usage for unit 1408 with usage 1.11481 . Total units tried = 18\n",
      "try to drop unit 1408 from 10 th HD.  usage down to 1.1148103171060484\n",
      "try to drop unit 1408 from 20 th HD.  usage down to 1.1148103171060484\n",
      "try to drop unit 1408 from 30 th HD.  usage down to 1.1148103171060484\n",
      "try to drop unit 1408 from 40 th HD.  usage down to 1.1148103171060484\n",
      "try to drop unit 1408 from 50 th HD.  usage down to 1.1148103171060484\n",
      "all done trying to reduce usage of unit 1408 final usage = 1.11481 38 sec elapsed 0 43 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.00018 0.06892\n",
      "starting to reduce usage for unit 1578 with usage 1.11431 . Total units tried = 19\n",
      "try to drop unit 1578 from 12 th HD.  usage down to 1.1143084619632344\n",
      "try to drop unit 1578 from 24 th HD.  usage down to 1.1143084619632344\n",
      "try to drop unit 1578 from 36 th HD.  usage down to 1.1143084619632344\n",
      "try to drop unit 1578 from 48 th HD.  usage down to 1.1143084619632344\n",
      "try to drop unit 1578 from 60 th HD.  usage down to 1.1143084619632344\n",
      "all done trying to reduce usage of unit 1578 final usage = 1.11431 39 sec elapsed 0 58 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00018 0.06892\n",
      "starting to reduce usage for unit 845 with usage 1.11356 . Total units tried = 20\n",
      "try to drop unit 845 from 11 th HD.  usage down to 1.1135585242328168\n",
      "try to drop unit 845 from 22 th HD.  usage down to 1.1135585242328168\n",
      "try to drop unit 845 from 33 th HD.  usage down to 1.1135585242328168\n",
      "try to drop unit 845 from 44 th HD.  usage down to 1.1135585242328168\n",
      "try to drop unit 845 from 55 th HD.  usage down to 1.1135585242328168\n",
      "all done trying to reduce usage of unit 845 final usage = 1.11356 41 sec elapsed 0 24 successful, failed patches, couldn't start= 33\n",
      "current avg and SD of usage are 1.00018 0.06892\n",
      "starting to reduce usage for unit 1271 with usage 1.11297 . Total units tried = 21\n",
      "try to drop unit 1271 from 7 th HD.  usage down to 1.1129679055953556\n",
      "try to drop unit 1271 from 14 th HD.  usage down to 1.107918628342154\n",
      "try to drop unit 1271 from 21 th HD.  usage down to 1.107918628342154\n",
      "try to drop unit 1271 from 28 th HD.  usage down to 1.1050099169021756\n",
      "try to drop unit 1271 from 35 th HD.  usage down to 1.1050099169021756\n",
      "all done trying to reduce usage of unit 1271 final usage = 1.09436 45 sec elapsed 5 31 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00019 0.06882\n",
      "starting to reduce usage for unit 1460 with usage 1.11088 . Total units tried = 22\n",
      "try to drop unit 1460 from 9 th HD.  usage down to 1.1108774114970619\n",
      "try to drop unit 1460 from 18 th HD.  usage down to 1.1108774114970619\n",
      "try to drop unit 1460 from 27 th HD.  usage down to 1.1108774114970619\n",
      "try to drop unit 1460 from 36 th HD.  usage down to 1.1056562972221715\n",
      "try to drop unit 1460 from 45 th HD.  usage down to 1.0989079956419403\n",
      "all done trying to reduce usage of unit 1460 final usage = 1.09891 47 sec elapsed 3 42 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00019 0.06875\n",
      "starting to reduce usage for unit 1405 with usage 1.11054 . Total units tried = 23\n",
      "try to drop unit 1405 from 10 th HD.  usage down to 1.1105394274212914\n",
      "try to drop unit 1405 from 20 th HD.  usage down to 1.1105394274212914\n",
      "try to drop unit 1405 from 30 th HD.  usage down to 1.1105394274212914\n",
      "try to drop unit 1405 from 40 th HD.  usage down to 1.1105394274212914\n",
      "try to drop unit 1405 from 50 th HD.  usage down to 1.1105394274212914\n",
      "all done trying to reduce usage of unit 1405 final usage = 1.10683 49 sec elapsed 1 49 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00019 0.06873\n",
      "starting to reduce usage for unit 1464 with usage 1.11164 . Total units tried = 24\n",
      "try to drop unit 1464 from 11 th HD.  usage down to 1.1077741031649722\n",
      "try to drop unit 1464 from 22 th HD.  usage down to 1.1077741031649722\n",
      "try to drop unit 1464 from 33 th HD.  usage down to 1.1027248259117706\n",
      "try to drop unit 1464 from 44 th HD.  usage down to 1.0961961570811154\n",
      "try to drop unit 1464 from 55 th HD.  usage down to 1.0961961570811154\n",
      "all done trying to reduce usage of unit 1464 final usage = 1.09303 51 sec elapsed 5 45 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00019 0.06865\n",
      "starting to reduce usage for unit 854 with usage 1.10922 . Total units tried = 25\n",
      "try to drop unit 854 from 13 th HD.  usage down to 1.109215940956229\n",
      "try to drop unit 854 from 26 th HD.  usage down to 1.109215940956229\n",
      "try to drop unit 854 from 39 th HD.  usage down to 1.109215940956229\n",
      "try to drop unit 854 from 52 th HD.  usage down to 1.109215940956229\n",
      "try to drop unit 854 from 65 th HD.  usage down to 1.109215940956229\n",
      "all done trying to reduce usage of unit 854 final usage = 1.10922 53 sec elapsed 0 54 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 1.00019 0.06865\n",
      "starting to reduce usage for unit 1536 with usage 1.1193 . Total units tried = 26\n",
      "try to drop unit 1536 from 13 th HD.  usage down to 1.1193019775339008\n",
      "try to drop unit 1536 from 26 th HD.  usage down to 1.1193019775339008\n",
      "try to drop unit 1536 from 39 th HD.  usage down to 1.1193019775339008\n",
      "try to drop unit 1536 from 52 th HD.  usage down to 1.1193019775339008\n",
      "try to drop unit 1536 from 65 th HD.  usage down to 1.1193019775339008\n",
      "all done trying to reduce usage of unit 1536 final usage = 1.1193 55 sec elapsed 0 57 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00019 0.06865\n",
      "starting to reduce usage for unit 1383 with usage 1.10901 . Total units tried = 27\n",
      "try to drop unit 1383 from 18 th HD.  usage down to 1.1090076881418647\n",
      "try to drop unit 1383 from 36 th HD.  usage down to 1.1090076881418647\n",
      "try to drop unit 1383 from 54 th HD.  usage down to 1.106514344337095\n",
      "try to drop unit 1383 from 72 th HD.  usage down to 1.1040039306295086\n",
      "try to drop unit 1383 from 90 th HD.  usage down to 1.0984516602399725\n",
      "all done trying to reduce usage of unit 1383 final usage = 1.09522 57 sec elapsed 5 72 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 1.00019 0.06858\n",
      "starting to reduce usage for unit 1407 with usage 1.10844 . Total units tried = 28\n",
      "try to drop unit 1407 from 11 th HD.  usage down to 1.1084386913813045\n",
      "try to drop unit 1407 from 22 th HD.  usage down to 1.1084386913813045\n",
      "try to drop unit 1407 from 33 th HD.  usage down to 1.1084386913813045\n",
      "try to drop unit 1407 from 44 th HD.  usage down to 1.1084386913813045\n",
      "try to drop unit 1407 from 55 th HD.  usage down to 1.1084386913813045\n",
      "all done trying to reduce usage of unit 1407 final usage = 1.10844 58 sec elapsed 0 54 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00019 0.06858\n",
      "starting to reduce usage for unit 1390 with usage 1.10628 . Total units tried = 29\n",
      "try to drop unit 1390 from 16 th HD.  usage down to 1.1062765036911826\n",
      "try to drop unit 1390 from 32 th HD.  usage down to 1.1062765036911826\n",
      "try to drop unit 1390 from 48 th HD.  usage down to 1.1062765036911826\n",
      "try to drop unit 1390 from 64 th HD.  usage down to 1.1062765036911826\n",
      "try to drop unit 1390 from 80 th HD.  usage down to 1.1017142876650194\n",
      "all done trying to reduce usage of unit 1390 final usage = 1.10171 61 sec elapsed 1 78 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00019 0.06856\n",
      "starting to reduce usage for unit 850 with usage 1.10613 . Total units tried = 30\n",
      "try to drop unit 850 from 12 th HD.  usage down to 1.106129702526955\n",
      "try to drop unit 850 from 24 th HD.  usage down to 1.106129702526955\n",
      "try to drop unit 850 from 36 th HD.  usage down to 1.106129702526955\n",
      "try to drop unit 850 from 48 th HD.  usage down to 1.1022901123867022\n",
      "try to drop unit 850 from 60 th HD.  usage down to 1.1022901123867022\n",
      "all done trying to reduce usage of unit 850 final usage = 1.09268 64 sec elapsed 3 41 successful, failed patches, couldn't start= 21\n",
      "current avg and SD of usage are 1.00018 0.06849\n",
      "starting to reduce usage for unit 957 with usage 1.10455 . Total units tried = 31\n",
      "try to drop unit 957 from 7 th HD.  usage down to 1.1045501675196472\n",
      "try to drop unit 957 from 14 th HD.  usage down to 1.1045501675196472\n",
      "try to drop unit 957 from 21 th HD.  usage down to 1.1045501675196472\n",
      "try to drop unit 957 from 28 th HD.  usage down to 1.1045501675196472\n",
      "try to drop unit 957 from 35 th HD.  usage down to 1.1045501675196472\n",
      "all done trying to reduce usage of unit 957 final usage = 1.10455 65 sec elapsed 0 27 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.00018 0.06849\n",
      "starting to reduce usage for unit 947 with usage 1.1045 . Total units tried = 32\n",
      "try to drop unit 947 from 8 th HD.  usage down to 1.1020454437796645\n",
      "try to drop unit 947 from 16 th HD.  usage down to 1.1020454437796645\n",
      "try to drop unit 947 from 24 th HD.  usage down to 1.1020454437796645\n",
      "try to drop unit 947 from 32 th HD.  usage down to 1.1020454437796645\n",
      "try to drop unit 947 from 40 th HD.  usage down to 1.1020454437796645\n",
      "all done trying to reduce usage of unit 947 final usage = 1.09258 67 sec elapsed 4 38 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00018 0.06843\n",
      "starting to reduce usage for unit 1269 with usage 1.10915 . Total units tried = 33\n",
      "try to drop unit 1269 from 12 th HD.  usage down to 1.1091476613449691\n",
      "try to drop unit 1269 from 24 th HD.  usage down to 1.1091476613449691\n",
      "try to drop unit 1269 from 36 th HD.  usage down to 1.1059555895182323\n",
      "try to drop unit 1269 from 48 th HD.  usage down to 1.1009063122650307\n",
      "try to drop unit 1269 from 60 th HD.  usage down to 1.0807569989801107\n",
      "all done trying to reduce usage of unit 1269 final usage = 1.07525 74 sec elapsed 9 50 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00018 0.0683\n",
      "starting to reduce usage for unit 1588 with usage 1.10379 . Total units tried = 34\n",
      "try to drop unit 1588 from 18 th HD.  usage down to 1.1037911258410606\n",
      "try to drop unit 1588 from 36 th HD.  usage down to 1.1037911258410606\n",
      "try to drop unit 1588 from 54 th HD.  usage down to 1.1037911258410606\n",
      "try to drop unit 1588 from 72 th HD.  usage down to 1.1037911258410606\n",
      "try to drop unit 1588 from 90 th HD.  usage down to 1.101030353558828\n",
      "all done trying to reduce usage of unit 1588 final usage = 1.09849 76 sec elapsed 2 52 successful, failed patches, couldn't start= 41\n",
      "current avg and SD of usage are 1.00018 0.06827\n",
      "starting to reduce usage for unit 1118 with usage 1.10362 . Total units tried = 35\n",
      "try to drop unit 1118 from 7 th HD.  usage down to 1.1036215648064136\n",
      "try to drop unit 1118 from 14 th HD.  usage down to 1.1036215648064136\n",
      "try to drop unit 1118 from 21 th HD.  usage down to 1.1036215648064136\n",
      "try to drop unit 1118 from 28 th HD.  usage down to 1.1036215648064136\n",
      "try to drop unit 1118 from 35 th HD.  usage down to 1.1036215648064136\n",
      "all done trying to reduce usage of unit 1118 final usage = 1.10362 77 sec elapsed 0 33 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00018 0.06827\n",
      "starting to reduce usage for unit 881 with usage 1.10358 . Total units tried = 36\n",
      "try to drop unit 881 from 8 th HD.  usage down to 1.1035840110202157\n",
      "try to drop unit 881 from 16 th HD.  usage down to 1.1035840110202157\n",
      "try to drop unit 881 from 24 th HD.  usage down to 1.1035840110202157\n",
      "try to drop unit 881 from 32 th HD.  usage down to 1.095201550743659\n",
      "try to drop unit 881 from 40 th HD.  usage down to 1.095201550743659\n",
      "all done trying to reduce usage of unit 881 final usage = 1.09118 83 sec elapsed 3 35 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00018 0.0682\n",
      "starting to reduce usage for unit 1582 with usage 1.10297 . Total units tried = 37\n",
      "try to drop unit 1582 from 20 th HD.  usage down to 1.1029672185317743\n",
      "try to drop unit 1582 from 40 th HD.  usage down to 1.1029672185317743\n",
      "try to drop unit 1582 from 60 th HD.  usage down to 1.1029672185317743\n",
      "try to drop unit 1582 from 80 th HD.  usage down to 1.1029672185317743\n",
      "try to drop unit 1582 from 100 th HD.  usage down to 1.099651105411236\n",
      "all done trying to reduce usage of unit 1582 final usage = 1.0972 86 sec elapsed 2 84 successful, failed patches, couldn't start= 17\n",
      "current avg and SD of usage are 1.00018 0.06817\n",
      "starting to reduce usage for unit 1299 with usage 1.10256 . Total units tried = 38\n",
      "try to drop unit 1299 from 17 th HD.  usage down to 1.1025609548447322\n",
      "try to drop unit 1299 from 34 th HD.  usage down to 1.1025609548447322\n",
      "try to drop unit 1299 from 51 th HD.  usage down to 1.100107440813201\n",
      "try to drop unit 1299 from 68 th HD.  usage down to 1.0942695340498643\n",
      "try to drop unit 1299 from 85 th HD.  usage down to 1.0878922183575173\n",
      "all done trying to reduce usage of unit 1299 final usage = 1.08789 90 sec elapsed 5 78 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00018 0.0681\n",
      "starting to reduce usage for unit 1537 with usage 1.10236 . Total units tried = 39\n",
      "try to drop unit 1537 from 14 th HD.  usage down to 1.1023606679850155\n",
      "try to drop unit 1537 from 28 th HD.  usage down to 1.1023606679850155\n",
      "try to drop unit 1537 from 42 th HD.  usage down to 1.1023606679850155\n",
      "try to drop unit 1537 from 56 th HD.  usage down to 1.1023606679850155\n",
      "try to drop unit 1537 from 70 th HD.  usage down to 1.1023606679850155\n",
      "all done trying to reduce usage of unit 1537 final usage = 1.09823 91 sec elapsed 1 59 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 1.00018 0.06808\n",
      "starting to reduce usage for unit 849 with usage 1.1019 . Total units tried = 40\n",
      "try to drop unit 849 from 12 th HD.  usage down to 1.1018963666283954\n",
      "try to drop unit 849 from 24 th HD.  usage down to 1.1018963666283954\n",
      "try to drop unit 849 from 36 th HD.  usage down to 1.1018963666283954\n",
      "try to drop unit 849 from 48 th HD.  usage down to 1.1018963666283954\n",
      "try to drop unit 849 from 60 th HD.  usage down to 1.1018963666283954\n",
      "all done trying to reduce usage of unit 849 final usage = 1.1019 93 sec elapsed 0 36 successful, failed patches, couldn't start= 25\n",
      "current avg and SD of usage are 1.00018 0.06808\n",
      "starting to reduce usage for unit 1305 with usage 1.10182 . Total units tried = 41\n",
      "try to drop unit 1305 from 15 th HD.  usage down to 1.1018178450754412\n",
      "try to drop unit 1305 from 30 th HD.  usage down to 1.1018178450754412\n",
      "try to drop unit 1305 from 45 th HD.  usage down to 1.1018178450754412\n",
      "try to drop unit 1305 from 60 th HD.  usage down to 1.1018178450754412\n",
      "try to drop unit 1305 from 75 th HD.  usage down to 1.1018178450754412\n",
      "all done trying to reduce usage of unit 1305 final usage = 1.09671 94 sec elapsed 1 65 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.00018 0.06804\n",
      "starting to reduce usage for unit 1515 with usage 1.10174 . Total units tried = 42\n",
      "try to drop unit 1515 from 10 th HD.  usage down to 1.101738185528963\n",
      "try to drop unit 1515 from 20 th HD.  usage down to 1.101738185528963\n",
      "try to drop unit 1515 from 30 th HD.  usage down to 1.0965170712540726\n",
      "try to drop unit 1515 from 40 th HD.  usage down to 1.0965170712540726\n",
      "try to drop unit 1515 from 50 th HD.  usage down to 1.0965170712540726\n",
      "all done trying to reduce usage of unit 1515 final usage = 1.09319 97 sec elapsed 2 49 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00018 0.068\n",
      "starting to reduce usage for unit 1081 with usage 1.10134 . Total units tried = 43\n",
      "try to drop unit 1081 from 12 th HD.  usage down to 1.10133647381601\n",
      "try to drop unit 1081 from 24 th HD.  usage down to 1.10133647381601\n",
      "try to drop unit 1081 from 36 th HD.  usage down to 1.10133647381601\n",
      "try to drop unit 1081 from 48 th HD.  usage down to 1.0926239954183286\n",
      "try to drop unit 1081 from 60 th HD.  usage down to 1.0926239954183286\n",
      "all done trying to reduce usage of unit 1081 final usage = 1.09262 100 sec elapsed 2 58 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00018 0.06796\n",
      "starting to reduce usage for unit 1603 with usage 1.1013 . Total units tried = 44\n",
      "try to drop unit 1603 from 12 th HD.  usage down to 1.1013000580233308\n",
      "try to drop unit 1603 from 24 th HD.  usage down to 1.1013000580233308\n",
      "try to drop unit 1603 from 36 th HD.  usage down to 1.1013000580233308\n",
      "try to drop unit 1603 from 48 th HD.  usage down to 1.1013000580233308\n",
      "try to drop unit 1603 from 60 th HD.  usage down to 1.1013000580233308\n",
      "all done trying to reduce usage of unit 1603 final usage = 1.09608 103 sec elapsed 1 50 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 1.00018 0.06793\n",
      "starting to reduce usage for unit 953 with usage 1.10055 . Total units tried = 45\n",
      "try to drop unit 953 from 9 th HD.  usage down to 1.1005467063123533\n",
      "try to drop unit 953 from 18 th HD.  usage down to 1.0995543759619382\n",
      "try to drop unit 953 from 27 th HD.  usage down to 1.097100861930407\n",
      "try to drop unit 953 from 36 th HD.  usage down to 1.097100861930407\n",
      "try to drop unit 953 from 45 th HD.  usage down to 1.097100861930407\n",
      "all done trying to reduce usage of unit 953 final usage = 1.0971 106 sec elapsed 2 46 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00018 0.06792\n",
      "starting to reduce usage for unit 951 with usage 1.10005 . Total units tried = 46\n",
      "try to drop unit 951 from 8 th HD.  usage down to 1.100048265150105\n",
      "try to drop unit 951 from 16 th HD.  usage down to 1.100048265150105\n",
      "try to drop unit 951 from 24 th HD.  usage down to 1.100048265150105\n",
      "try to drop unit 951 from 32 th HD.  usage down to 1.0937608509459267\n",
      "try to drop unit 951 from 40 th HD.  usage down to 1.090902211220878\n",
      "all done trying to reduce usage of unit 951 final usage = 1.08845 107 sec elapsed 4 35 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00018 0.06787\n",
      "starting to reduce usage for unit 853 with usage 1.09887 . Total units tried = 47\n",
      "try to drop unit 853 from 11 th HD.  usage down to 1.0988704418557425\n",
      "try to drop unit 853 from 22 th HD.  usage down to 1.0988704418557425\n",
      "try to drop unit 853 from 33 th HD.  usage down to 1.0988704418557425\n",
      "try to drop unit 853 from 44 th HD.  usage down to 1.0988704418557425\n",
      "try to drop unit 853 from 55 th HD.  usage down to 1.0942444981923969\n",
      "all done trying to reduce usage of unit 853 final usage = 1.09424 112 sec elapsed 1 35 successful, failed patches, couldn't start= 20\n",
      "current avg and SD of usage are 1.00019 0.06782\n",
      "starting to reduce usage for unit 1564 with usage 1.11891 . Total units tried = 48\n",
      "try to drop unit 1564 from 14 th HD.  usage down to 1.1145542685637964\n",
      "try to drop unit 1564 from 28 th HD.  usage down to 1.1145542685637964\n",
      "try to drop unit 1564 from 42 th HD.  usage down to 1.1145542685637964\n",
      "try to drop unit 1564 from 56 th HD.  usage down to 1.1145542685637964\n",
      "try to drop unit 1564 from 70 th HD.  usage down to 1.1100887819869283\n",
      "all done trying to reduce usage of unit 1564 final usage = 1.11009 113 sec elapsed 2 18 successful, failed patches, couldn't start= 52\n",
      "current avg and SD of usage are 1.00019 0.06779\n",
      "starting to reduce usage for unit 1511 with usage 1.09869 . Total units tried = 49\n",
      "try to drop unit 1511 from 9 th HD.  usage down to 1.098690638879408\n",
      "try to drop unit 1511 from 18 th HD.  usage down to 1.098690638879408\n",
      "try to drop unit 1511 from 27 th HD.  usage down to 1.098690638879408\n",
      "try to drop unit 1511 from 36 th HD.  usage down to 1.098690638879408\n",
      "try to drop unit 1511 from 45 th HD.  usage down to 1.0949341222661968\n",
      "all done trying to reduce usage of unit 1511 final usage = 1.09493 116 sec elapsed 1 41 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00019 0.06777\n",
      "starting to reduce usage for unit 1260 with usage 1.10824 . Total units tried = 50\n",
      "try to drop unit 1260 from 13 th HD.  usage down to 1.1082429564956748\n",
      "try to drop unit 1260 from 26 th HD.  usage down to 1.1082429564956748\n",
      "try to drop unit 1260 from 39 th HD.  usage down to 1.1082429564956748\n",
      "try to drop unit 1260 from 52 th HD.  usage down to 1.1082429564956748\n",
      "try to drop unit 1260 from 65 th HD.  usage down to 1.105079334506966\n",
      "all done trying to reduce usage of unit 1260 final usage = 1.10508 118 sec elapsed 1 22 successful, failed patches, couldn't start= 44\n",
      "current avg and SD of usage are 1.00019 0.06775\n",
      "starting to reduce usage for unit 1512 with usage 1.09747 . Total units tried = 51\n",
      "try to drop unit 1512 from 9 th HD.  usage down to 1.0974718478182905\n",
      "try to drop unit 1512 from 18 th HD.  usage down to 1.0949352602597178\n",
      "try to drop unit 1512 from 27 th HD.  usage down to 1.0949352602597178\n",
      "try to drop unit 1512 from 36 th HD.  usage down to 1.0949352602597178\n",
      "try to drop unit 1512 from 45 th HD.  usage down to 1.078574327406601\n",
      "all done trying to reduce usage of unit 1512 final usage = 1.07857 126 sec elapsed 5 34 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00018 0.06765\n",
      "starting to reduce usage for unit 1423 with usage 1.0969 . Total units tried = 52\n",
      "try to drop unit 1423 from 9 th HD.  usage down to 1.0968982990836456\n",
      "try to drop unit 1423 from 18 th HD.  usage down to 1.0968982990836456\n",
      "try to drop unit 1423 from 27 th HD.  usage down to 1.0968982990836456\n",
      "try to drop unit 1423 from 36 th HD.  usage down to 1.0968982990836456\n",
      "try to drop unit 1423 from 45 th HD.  usage down to 1.0901499975034143\n",
      "all done trying to reduce usage of unit 1423 final usage = 1.09015 127 sec elapsed 2 42 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00018 0.06761\n",
      "starting to reduce usage for unit 952 with usage 1.09732 . Total units tried = 53\n",
      "try to drop unit 952 from 9 th HD.  usage down to 1.0973216326735025\n",
      "try to drop unit 952 from 18 th HD.  usage down to 1.0973216326735025\n",
      "try to drop unit 952 from 27 th HD.  usage down to 1.0973216326735025\n",
      "try to drop unit 952 from 36 th HD.  usage down to 1.0973216326735025\n",
      "try to drop unit 952 from 45 th HD.  usage down to 1.0973216326735025\n",
      "all done trying to reduce usage of unit 952 final usage = 1.08588 130 sec elapsed 3 41 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00018 0.06756\n",
      "starting to reduce usage for unit 1245 with usage 1.09637 . Total units tried = 54\n",
      "try to drop unit 1245 from 19 th HD.  usage down to 1.0963679941028066\n",
      "try to drop unit 1245 from 38 th HD.  usage down to 1.0963679941028066\n",
      "try to drop unit 1245 from 57 th HD.  usage down to 1.0963679941028066\n",
      "try to drop unit 1245 from 76 th HD.  usage down to 1.0963679941028066\n",
      "try to drop unit 1245 from 95 th HD.  usage down to 1.093774506868178\n",
      "all done trying to reduce usage of unit 1245 final usage = 1.09377 133 sec elapsed 1 89 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00018 0.06755\n",
      "starting to reduce usage for unit 940 with usage 1.0963 . Total units tried = 55\n",
      "try to drop unit 940 from 9 th HD.  usage down to 1.0963019904785807\n",
      "try to drop unit 940 from 18 th HD.  usage down to 1.0963019904785807\n",
      "try to drop unit 940 from 27 th HD.  usage down to 1.0963019904785807\n",
      "try to drop unit 940 from 36 th HD.  usage down to 1.0963019904785807\n",
      "try to drop unit 940 from 45 th HD.  usage down to 1.0963019904785807\n",
      "all done trying to reduce usage of unit 940 final usage = 1.0963 134 sec elapsed 0 35 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 1.00018 0.06755\n",
      "starting to reduce usage for unit 1427 with usage 1.09642 . Total units tried = 56\n",
      "try to drop unit 1427 from 10 th HD.  usage down to 1.0964248937788612\n",
      "try to drop unit 1427 from 20 th HD.  usage down to 1.0964248937788612\n",
      "try to drop unit 1427 from 30 th HD.  usage down to 1.0964248937788612\n",
      "try to drop unit 1427 from 40 th HD.  usage down to 1.0964248937788612\n",
      "try to drop unit 1427 from 50 th HD.  usage down to 1.0964248937788612\n",
      "all done trying to reduce usage of unit 1427 final usage = 1.08946 136 sec elapsed 2 35 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 1.00018 0.06752\n",
      "starting to reduce usage for unit 939 with usage 1.09621 . Total units tried = 57\n",
      "try to drop unit 939 from 14 th HD.  usage down to 1.0962143649774532\n",
      "try to drop unit 939 from 28 th HD.  usage down to 1.0962143649774532\n",
      "try to drop unit 939 from 42 th HD.  usage down to 1.0962143649774532\n",
      "try to drop unit 939 from 56 th HD.  usage down to 1.0962143649774532\n",
      "try to drop unit 939 from 70 th HD.  usage down to 1.0962143649774532\n",
      "all done trying to reduce usage of unit 939 final usage = 1.09621 137 sec elapsed 0 59 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 1.00018 0.06752\n",
      "starting to reduce usage for unit 1587 with usage 1.09626 . Total units tried = 58\n",
      "try to drop unit 1587 from 16 th HD.  usage down to 1.0962598847183014\n",
      "try to drop unit 1587 from 32 th HD.  usage down to 1.0962598847183014\n",
      "try to drop unit 1587 from 48 th HD.  usage down to 1.0962598847183014\n",
      "try to drop unit 1587 from 64 th HD.  usage down to 1.0962598847183014\n",
      "try to drop unit 1587 from 80 th HD.  usage down to 1.093749471010715\n",
      "all done trying to reduce usage of unit 1587 final usage = 1.09375 139 sec elapsed 1 57 successful, failed patches, couldn't start= 26\n",
      "current avg and SD of usage are 1.00018 0.06751\n",
      "starting to reduce usage for unit 882 with usage 1.09546 . Total units tried = 59\n",
      "try to drop unit 882 from 11 th HD.  usage down to 1.0954644272470357\n",
      "try to drop unit 882 from 22 th HD.  usage down to 1.0954644272470357\n",
      "try to drop unit 882 from 33 th HD.  usage down to 1.0954644272470357\n",
      "try to drop unit 882 from 44 th HD.  usage down to 1.0917079106338246\n",
      "try to drop unit 882 from 55 th HD.  usage down to 1.0917079106338246\n",
      "all done trying to reduce usage of unit 882 final usage = 1.08831 142 sec elapsed 2 46 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00018 0.06747\n",
      "starting to reduce usage for unit 1595 with usage 1.09542 . Total units tried = 60\n",
      "try to drop unit 1595 from 14 th HD.  usage down to 1.095417769512672\n",
      "try to drop unit 1595 from 28 th HD.  usage down to 1.095417769512672\n",
      "try to drop unit 1595 from 42 th HD.  usage down to 1.095417769512672\n",
      "try to drop unit 1595 from 56 th HD.  usage down to 1.0906814404777776\n",
      "try to drop unit 1595 from 70 th HD.  usage down to 1.0906814404777776\n",
      "all done trying to reduce usage of unit 1595 final usage = 1.09068 145 sec elapsed 1 60 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.00018 0.06745\n",
      "starting to reduce usage for unit 1462 with usage 1.09524 . Total units tried = 61\n",
      "try to drop unit 1462 from 12 th HD.  usage down to 1.0952402425233756\n",
      "try to drop unit 1462 from 24 th HD.  usage down to 1.0913300967848132\n",
      "try to drop unit 1462 from 36 th HD.  usage down to 1.0887935092262404\n",
      "try to drop unit 1462 from 48 th HD.  usage down to 1.0887935092262404\n",
      "try to drop unit 1462 from 60 th HD.  usage down to 1.0863058553890763\n",
      "all done trying to reduce usage of unit 1462 final usage = 1.08631 147 sec elapsed 3 47 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 1.00018 0.06741\n",
      "starting to reduce usage for unit 1534 with usage 1.09509 . Total units tried = 62\n",
      "try to drop unit 1534 from 15 th HD.  usage down to 1.0950866133980242\n",
      "try to drop unit 1534 from 30 th HD.  usage down to 1.0950866133980242\n",
      "try to drop unit 1534 from 45 th HD.  usage down to 1.0950866133980242\n",
      "try to drop unit 1534 from 60 th HD.  usage down to 1.0950866133980242\n",
      "try to drop unit 1534 from 75 th HD.  usage down to 1.0950866133980242\n",
      "all done trying to reduce usage of unit 1534 final usage = 1.09099 148 sec elapsed 1 52 successful, failed patches, couldn't start= 22\n",
      "current avg and SD of usage are 1.00019 0.06739\n",
      "starting to reduce usage for unit 1302 with usage 1.09488 . Total units tried = 63\n",
      "try to drop unit 1302 from 15 th HD.  usage down to 1.094884050551268\n",
      "try to drop unit 1302 from 30 th HD.  usage down to 1.094884050551268\n",
      "try to drop unit 1302 from 45 th HD.  usage down to 1.094884050551268\n",
      "try to drop unit 1302 from 60 th HD.  usage down to 1.094884050551268\n",
      "try to drop unit 1302 from 75 th HD.  usage down to 1.094884050551268\n",
      "all done trying to reduce usage of unit 1302 final usage = 1.09488 150 sec elapsed 0 70 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00019 0.06739\n",
      "starting to reduce usage for unit 1404 with usage 1.09393 . Total units tried = 64\n",
      "try to drop unit 1404 from 10 th HD.  usage down to 1.093932687967612\n",
      "try to drop unit 1404 from 20 th HD.  usage down to 1.093932687967612\n",
      "try to drop unit 1404 from 30 th HD.  usage down to 1.093932687967612\n",
      "try to drop unit 1404 from 40 th HD.  usage down to 1.093932687967612\n",
      "try to drop unit 1404 from 50 th HD.  usage down to 1.093932687967612\n",
      "all done trying to reduce usage of unit 1404 final usage = 1.09393 151 sec elapsed 0 45 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00019 0.06739\n",
      "starting to reduce usage for unit 1306 with usage 1.09348 . Total units tried = 65\n",
      "try to drop unit 1306 from 16 th HD.  usage down to 1.0934752145721247\n",
      "try to drop unit 1306 from 32 th HD.  usage down to 1.0934752145721247\n",
      "try to drop unit 1306 from 48 th HD.  usage down to 1.0934752145721247\n",
      "try to drop unit 1306 from 64 th HD.  usage down to 1.0934752145721247\n",
      "try to drop unit 1306 from 80 th HD.  usage down to 1.0934752145721247\n",
      "all done trying to reduce usage of unit 1306 final usage = 1.09348 153 sec elapsed 0 69 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 1.00019 0.06739\n",
      "starting to reduce usage for unit 1263 with usage 1.10404 . Total units tried = 66\n",
      "try to drop unit 1263 from 15 th HD.  usage down to 1.1040357944481016\n",
      "try to drop unit 1263 from 30 th HD.  usage down to 1.1040357944481016\n",
      "try to drop unit 1263 from 45 th HD.  usage down to 1.1040357944481016\n",
      "try to drop unit 1263 from 60 th HD.  usage down to 1.1040357944481016\n",
      "try to drop unit 1263 from 75 th HD.  usage down to 1.0999412937591184\n",
      "all done trying to reduce usage of unit 1263 final usage = 1.09994 155 sec elapsed 1 61 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 1.00019 0.06737\n",
      "starting to reduce usage for unit 1605 with usage 1.09265 . Total units tried = 67\n",
      "try to drop unit 1605 from 12 th HD.  usage down to 1.092653583249874\n",
      "try to drop unit 1605 from 24 th HD.  usage down to 1.092653583249874\n",
      "try to drop unit 1605 from 36 th HD.  usage down to 1.092653583249874\n",
      "try to drop unit 1605 from 48 th HD.  usage down to 1.092653583249874\n",
      "try to drop unit 1605 from 60 th HD.  usage down to 1.092653583249874\n",
      "all done trying to reduce usage of unit 1605 final usage = 1.09265 156 sec elapsed 0 45 successful, failed patches, couldn't start= 17\n",
      "current avg and SD of usage are 1.00019 0.06737\n",
      "starting to reduce usage for unit 866 with usage 1.09221 . Total units tried = 68\n",
      "try to drop unit 866 from 13 th HD.  usage down to 1.0922086277831184\n",
      "try to drop unit 866 from 26 th HD.  usage down to 1.0922086277831184\n",
      "try to drop unit 866 from 39 th HD.  usage down to 1.0922086277831184\n",
      "try to drop unit 866 from 52 th HD.  usage down to 1.0922086277831184\n",
      "try to drop unit 866 from 65 th HD.  usage down to 1.0922086277831184\n",
      "all done trying to reduce usage of unit 866 final usage = 1.09221 157 sec elapsed 0 38 successful, failed patches, couldn't start= 29\n",
      "current avg and SD of usage are 1.00019 0.06737\n",
      "starting to reduce usage for unit 956 with usage 1.09184 . Total units tried = 69\n",
      "try to drop unit 956 from 9 th HD.  usage down to 1.0882893780963865\n",
      "try to drop unit 956 from 18 th HD.  usage down to 1.0882893780963865\n",
      "try to drop unit 956 from 27 th HD.  usage down to 1.0882893780963865\n",
      "try to drop unit 956 from 36 th HD.  usage down to 1.0882893780963865\n",
      "try to drop unit 956 from 45 th HD.  usage down to 1.086426482702316\n",
      "all done trying to reduce usage of unit 956 final usage = 1.08643 159 sec elapsed 2 41 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00018 0.06734\n",
      "starting to reduce usage for unit 1570 with usage 1.0977 . Total units tried = 70\n",
      "try to drop unit 1570 from 14 th HD.  usage down to 1.0976983085289935\n",
      "try to drop unit 1570 from 28 th HD.  usage down to 1.0976983085289935\n",
      "try to drop unit 1570 from 42 th HD.  usage down to 1.0976983085289935\n",
      "try to drop unit 1570 from 56 th HD.  usage down to 1.0976983085289935\n",
      "try to drop unit 1570 from 70 th HD.  usage down to 1.0976983085289935\n",
      "all done trying to reduce usage of unit 1570 final usage = 1.0977 161 sec elapsed 0 55 successful, failed patches, couldn't start= 16\n",
      "current avg and SD of usage are 1.00018 0.06734\n",
      "starting to reduce usage for unit 1183 with usage 1.09172 . Total units tried = 71\n",
      "try to drop unit 1183 from 16 th HD.  usage down to 1.0917227045496043\n",
      "try to drop unit 1183 from 32 th HD.  usage down to 1.0917227045496043\n",
      "try to drop unit 1183 from 48 th HD.  usage down to 1.0917227045496043\n",
      "try to drop unit 1183 from 64 th HD.  usage down to 1.0844850657552922\n",
      "try to drop unit 1183 from 80 th HD.  usage down to 1.053085548520597\n",
      "all done trying to reduce usage of unit 1183 final usage = 1.05309 169 sec elapsed 10 57 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 1.00019 0.06719\n",
      "starting to reduce usage for unit 1406 with usage 1.09146 . Total units tried = 72\n",
      "try to drop unit 1406 from 10 th HD.  usage down to 1.0914586900527001\n",
      "try to drop unit 1406 from 20 th HD.  usage down to 1.0885772904572288\n",
      "try to drop unit 1406 from 30 th HD.  usage down to 1.0885772904572288\n",
      "try to drop unit 1406 from 40 th HD.  usage down to 1.0845567593471177\n",
      "try to drop unit 1406 from 50 th HD.  usage down to 1.0845567593471177\n",
      "all done trying to reduce usage of unit 1406 final usage = 1.08456 171 sec elapsed 2 47 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00019 0.06717\n",
      "starting to reduce usage for unit 1586 with usage 1.09134 . Total units tried = 73\n",
      "try to drop unit 1586 from 16 th HD.  usage down to 1.0913403387265082\n",
      "try to drop unit 1586 from 32 th HD.  usage down to 1.0913403387265082\n",
      "try to drop unit 1586 from 48 th HD.  usage down to 1.0913403387265082\n",
      "try to drop unit 1586 from 64 th HD.  usage down to 1.0913403387265082\n",
      "try to drop unit 1586 from 80 th HD.  usage down to 1.090348008376093\n",
      "all done trying to reduce usage of unit 1586 final usage = 1.09035 174 sec elapsed 1 78 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00019 0.06716\n",
      "starting to reduce usage for unit 1126 with usage 1.09113 . Total units tried = 74\n",
      "try to drop unit 1126 from 12 th HD.  usage down to 1.0888344769930038\n",
      "try to drop unit 1126 from 24 th HD.  usage down to 1.0888344769930038\n",
      "try to drop unit 1126 from 36 th HD.  usage down to 1.0888344769930038\n",
      "try to drop unit 1126 from 48 th HD.  usage down to 1.0888344769930038\n",
      "try to drop unit 1126 from 60 th HD.  usage down to 1.084994886852751\n",
      "all done trying to reduce usage of unit 1126 final usage = 1.08091 176 sec elapsed 3 54 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00019 0.06712\n",
      "starting to reduce usage for unit 1459 with usage 1.09057 . Total units tried = 75\n",
      "try to drop unit 1459 from 8 th HD.  usage down to 1.0905676411256637\n",
      "try to drop unit 1459 from 16 th HD.  usage down to 1.0905676411256637\n",
      "try to drop unit 1459 from 24 th HD.  usage down to 1.0905676411256637\n",
      "try to drop unit 1459 from 32 th HD.  usage down to 1.0905676411256637\n",
      "try to drop unit 1459 from 40 th HD.  usage down to 1.0905676411256637\n",
      "all done trying to reduce usage of unit 1459 final usage = 1.09057 177 sec elapsed 0 45 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00019 0.06712\n",
      "starting to reduce usage for unit 1379 with usage 1.09055 . Total units tried = 76\n",
      "try to drop unit 1379 from 18 th HD.  usage down to 1.0905539852034187\n",
      "try to drop unit 1379 from 36 th HD.  usage down to 1.0905539852034187\n",
      "try to drop unit 1379 from 54 th HD.  usage down to 1.0821419370953123\n",
      "try to drop unit 1379 from 72 th HD.  usage down to 1.0821419370953123\n",
      "try to drop unit 1379 from 90 th HD.  usage down to 1.0699699583934317\n",
      "all done trying to reduce usage of unit 1379 final usage = 1.06997 182 sec elapsed 5 77 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00019 0.06701\n",
      "starting to reduce usage for unit 1048 with usage 1.09036 . Total units tried = 77\n",
      "try to drop unit 1048 from 11 th HD.  usage down to 1.0903639402853857\n",
      "try to drop unit 1048 from 22 th HD.  usage down to 1.086203435972177\n",
      "try to drop unit 1048 from 33 th HD.  usage down to 1.086203435972177\n",
      "try to drop unit 1048 from 44 th HD.  usage down to 1.0823331200068547\n",
      "try to drop unit 1048 from 55 th HD.  usage down to 1.0823331200068547\n",
      "all done trying to reduce usage of unit 1048 final usage = 1.0787 186 sec elapsed 4 44 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.00019 0.06696\n",
      "starting to reduce usage for unit 1319 with usage 1.09026 . Total units tried = 78\n",
      "try to drop unit 1319 from 10 th HD.  usage down to 1.0902649348490476\n",
      "try to drop unit 1319 from 20 th HD.  usage down to 1.0902649348490476\n",
      "try to drop unit 1319 from 30 th HD.  usage down to 1.0902649348490476\n",
      "try to drop unit 1319 from 40 th HD.  usage down to 1.0871013128603388\n",
      "try to drop unit 1319 from 50 th HD.  usage down to 1.082866838968258\n",
      "all done trying to reduce usage of unit 1319 final usage = 1.0701 189 sec elapsed 5 47 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00019 0.06687\n",
      "starting to reduce usage for unit 1509 with usage 1.09023 . Total units tried = 79\n",
      "try to drop unit 1509 from 16 th HD.  usage down to 1.0902251050758112\n",
      "try to drop unit 1509 from 32 th HD.  usage down to 1.0902251050758112\n",
      "try to drop unit 1509 from 48 th HD.  usage down to 1.0902251050758112\n",
      "try to drop unit 1509 from 64 th HD.  usage down to 1.0902251050758112\n",
      "try to drop unit 1509 from 80 th HD.  usage down to 1.0902251050758112\n",
      "all done trying to reduce usage of unit 1509 final usage = 1.09023 190 sec elapsed 0 83 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00019 0.06687\n",
      "starting to reduce usage for unit 852 with usage 1.08976 . Total units tried = 80\n",
      "try to drop unit 852 from 8 th HD.  usage down to 1.089755113751586\n",
      "try to drop unit 852 from 16 th HD.  usage down to 1.089755113751586\n",
      "try to drop unit 852 from 24 th HD.  usage down to 1.089755113751586\n",
      "try to drop unit 852 from 32 th HD.  usage down to 1.089755113751586\n",
      "try to drop unit 852 from 40 th HD.  usage down to 1.077255392915623\n",
      "all done trying to reduce usage of unit 852 final usage = 1.07366 194 sec elapsed 5 32 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00019 0.06678\n",
      "starting to reduce usage for unit 1124 with usage 1.0894 . Total units tried = 81\n",
      "try to drop unit 1124 from 11 th HD.  usage down to 1.0894046117470815\n",
      "try to drop unit 1124 from 22 th HD.  usage down to 1.0894046117470815\n",
      "try to drop unit 1124 from 33 th HD.  usage down to 1.086206849952739\n",
      "try to drop unit 1124 from 44 th HD.  usage down to 1.086206849952739\n",
      "try to drop unit 1124 from 55 th HD.  usage down to 1.086206849952739\n",
      "all done trying to reduce usage of unit 1124 final usage = 1.08621 196 sec elapsed 1 54 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00019 0.06676\n",
      "starting to reduce usage for unit 1425 with usage 1.08926 . Total units tried = 82\n",
      "try to drop unit 1425 from 14 th HD.  usage down to 1.0892566725893373\n",
      "try to drop unit 1425 from 28 th HD.  usage down to 1.0892566725893373\n",
      "try to drop unit 1425 from 42 th HD.  usage down to 1.0892566725893373\n",
      "try to drop unit 1425 from 56 th HD.  usage down to 1.0892566725893373\n",
      "try to drop unit 1425 from 70 th HD.  usage down to 1.084882225494159\n",
      "all done trying to reduce usage of unit 1425 final usage = 1.08488 197 sec elapsed 1 66 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00019 0.06674\n",
      "starting to reduce usage for unit 1372 with usage 1.08907 . Total units tried = 83\n",
      "try to drop unit 1372 from 14 th HD.  usage down to 1.0890689036583543\n",
      "try to drop unit 1372 from 28 th HD.  usage down to 1.0890689036583543\n",
      "try to drop unit 1372 from 42 th HD.  usage down to 1.0890689036583543\n",
      "try to drop unit 1372 from 56 th HD.  usage down to 1.0890689036583543\n",
      "try to drop unit 1372 from 70 th HD.  usage down to 1.0812622681034831\n",
      "all done trying to reduce usage of unit 1372 final usage = 1.07277 199 sec elapsed 4 49 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 1.00019 0.06665\n",
      "starting to reduce usage for unit 1508 with usage 1.08877 . Total units tried = 84\n",
      "try to drop unit 1508 from 17 th HD.  usage down to 1.0887741633363823\n",
      "try to drop unit 1508 from 34 th HD.  usage down to 1.0887741633363823\n",
      "try to drop unit 1508 from 51 th HD.  usage down to 1.0887741633363823\n",
      "try to drop unit 1508 from 68 th HD.  usage down to 1.0887741633363823\n",
      "try to drop unit 1508 from 85 th HD.  usage down to 1.0887741633363823\n",
      "all done trying to reduce usage of unit 1508 final usage = 1.08877 201 sec elapsed 0 80 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00019 0.06665\n",
      "starting to reduce usage for unit 1123 with usage 1.08852 . Total units tried = 85\n",
      "try to drop unit 1123 from 10 th HD.  usage down to 1.0885238047617356\n",
      "try to drop unit 1123 from 20 th HD.  usage down to 1.0885238047617356\n",
      "try to drop unit 1123 from 30 th HD.  usage down to 1.0885238047617356\n",
      "try to drop unit 1123 from 40 th HD.  usage down to 1.0885238047617356\n",
      "try to drop unit 1123 from 50 th HD.  usage down to 1.082384329715302\n",
      "all done trying to reduce usage of unit 1123 final usage = 1.08238 203 sec elapsed 1 43 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00019 0.06663\n",
      "starting to reduce usage for unit 1584 with usage 1.0883 . Total units tried = 86\n",
      "try to drop unit 1584 from 16 th HD.  usage down to 1.0882973440510346\n",
      "try to drop unit 1584 from 32 th HD.  usage down to 1.0844076821958522\n",
      "try to drop unit 1584 from 48 th HD.  usage down to 1.0844076821958522\n",
      "try to drop unit 1584 from 64 th HD.  usage down to 1.0844076821958522\n",
      "try to drop unit 1584 from 80 th HD.  usage down to 1.0754027394632435\n",
      "all done trying to reduce usage of unit 1584 final usage = 1.0754 205 sec elapsed 3 56 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 1.00019 0.06657\n",
      "starting to reduce usage for unit 1486 with usage 1.08829 . Total units tried = 87\n",
      "try to drop unit 1486 from 16 th HD.  usage down to 1.088290516089908\n",
      "try to drop unit 1486 from 32 th HD.  usage down to 1.088290516089908\n",
      "try to drop unit 1486 from 48 th HD.  usage down to 1.088290516089908\n",
      "try to drop unit 1486 from 64 th HD.  usage down to 1.088290516089908\n",
      "try to drop unit 1486 from 80 th HD.  usage down to 1.088290516089908\n",
      "all done trying to reduce usage of unit 1486 final usage = 1.08829 207 sec elapsed 0 76 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00019 0.06657\n",
      "starting to reduce usage for unit 1492 with usage 1.08793 . Total units tried = 88\n",
      "try to drop unit 1492 from 11 th HD.  usage down to 1.08792635816315\n",
      "try to drop unit 1492 from 22 th HD.  usage down to 1.0853897706045772\n",
      "try to drop unit 1492 from 33 th HD.  usage down to 1.0853897706045772\n",
      "try to drop unit 1492 from 44 th HD.  usage down to 1.0775797210691427\n",
      "try to drop unit 1492 from 55 th HD.  usage down to 1.073817514488326\n",
      "all done trying to reduce usage of unit 1492 final usage = 1.07382 210 sec elapsed 4 48 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00019 0.06651\n",
      "starting to reduce usage for unit 941 with usage 1.08732 . Total units tried = 89\n",
      "try to drop unit 941 from 9 th HD.  usage down to 1.0873163936358317\n",
      "try to drop unit 941 from 18 th HD.  usage down to 1.0873163936358317\n",
      "try to drop unit 941 from 27 th HD.  usage down to 1.0873163936358317\n",
      "try to drop unit 941 from 36 th HD.  usage down to 1.0873163936358317\n",
      "try to drop unit 941 from 45 th HD.  usage down to 1.0873163936358317\n",
      "all done trying to reduce usage of unit 941 final usage = 1.08732 211 sec elapsed 0 38 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00019 0.06651\n",
      "starting to reduce usage for unit 1303 with usage 1.08735 . Total units tried = 90\n",
      "try to drop unit 1303 from 11 th HD.  usage down to 1.0873471194609012\n",
      "try to drop unit 1303 from 22 th HD.  usage down to 1.0873471194609012\n",
      "try to drop unit 1303 from 33 th HD.  usage down to 1.0873471194609012\n",
      "try to drop unit 1303 from 44 th HD.  usage down to 1.0873471194609012\n",
      "try to drop unit 1303 from 55 th HD.  usage down to 1.0873471194609012\n",
      "all done trying to reduce usage of unit 1303 final usage = 1.08418 213 sec elapsed 1 55 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00019 0.06649\n",
      "starting to reduce usage for unit 1477 with usage 1.08714 . Total units tried = 91\n",
      "try to drop unit 1477 from 19 th HD.  usage down to 1.0871445566141427\n",
      "try to drop unit 1477 from 38 th HD.  usage down to 1.0871445566141427\n",
      "try to drop unit 1477 from 57 th HD.  usage down to 1.0871445566141427\n",
      "try to drop unit 1477 from 76 th HD.  usage down to 1.0871445566141427\n",
      "try to drop unit 1477 from 95 th HD.  usage down to 1.0829021167674138\n",
      "all done trying to reduce usage of unit 1477 final usage = 1.0829 215 sec elapsed 1 82 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 1.00019 0.06646\n",
      "starting to reduce usage for unit 1463 with usage 1.08697 . Total units tried = 92\n",
      "try to drop unit 1463 from 8 th HD.  usage down to 1.0869681676183678\n",
      "try to drop unit 1463 from 16 th HD.  usage down to 1.0869681676183678\n",
      "try to drop unit 1463 from 24 th HD.  usage down to 1.0869681676183678\n",
      "try to drop unit 1463 from 32 th HD.  usage down to 1.0869681676183678\n",
      "try to drop unit 1463 from 40 th HD.  usage down to 1.082997708223186\n",
      "all done trying to reduce usage of unit 1463 final usage = 1.083 217 sec elapsed 1 37 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00019 0.06643\n",
      "starting to reduce usage for unit 868 with usage 1.08714 . Total units tried = 93\n",
      "try to drop unit 868 from 7 th HD.  usage down to 1.0871434186206201\n",
      "try to drop unit 868 from 14 th HD.  usage down to 1.0871434186206201\n",
      "try to drop unit 868 from 21 th HD.  usage down to 1.081327133734184\n",
      "try to drop unit 868 from 28 th HD.  usage down to 1.081327133734184\n",
      "try to drop unit 868 from 35 th HD.  usage down to 1.081327133734184\n",
      "all done trying to reduce usage of unit 868 final usage = 1.08133 218 sec elapsed 1 35 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00019 0.06641\n",
      "starting to reduce usage for unit 838 with usage 1.08658 . Total units tried = 94\n",
      "try to drop unit 838 from 10 th HD.  usage down to 1.0865823878147065\n",
      "try to drop unit 838 from 20 th HD.  usage down to 1.0865823878147065\n",
      "try to drop unit 838 from 30 th HD.  usage down to 1.0865823878147065\n",
      "try to drop unit 838 from 40 th HD.  usage down to 1.0865823878147065\n",
      "try to drop unit 838 from 50 th HD.  usage down to 1.0865823878147065\n",
      "all done trying to reduce usage of unit 838 final usage = 1.08658 219 sec elapsed 0 23 successful, failed patches, couldn't start= 32\n",
      "current avg and SD of usage are 1.00019 0.06641\n",
      "starting to reduce usage for unit 937 with usage 1.08685 . Total units tried = 95\n",
      "try to drop unit 937 from 11 th HD.  usage down to 1.0868543682662561\n",
      "try to drop unit 937 from 22 th HD.  usage down to 1.0868543682662561\n",
      "try to drop unit 937 from 33 th HD.  usage down to 1.0868543682662561\n",
      "try to drop unit 937 from 44 th HD.  usage down to 1.0868543682662561\n",
      "try to drop unit 937 from 55 th HD.  usage down to 1.0868543682662561\n",
      "all done trying to reduce usage of unit 937 final usage = 1.06816 225 sec elapsed 3 45 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.00019 0.06633\n",
      "starting to reduce usage for unit 1566 with usage 1.08634 . Total units tried = 96\n",
      "try to drop unit 1566 from 17 th HD.  usage down to 1.086339995194714\n",
      "try to drop unit 1566 from 34 th HD.  usage down to 1.086339995194714\n",
      "try to drop unit 1566 from 51 th HD.  usage down to 1.086339995194714\n",
      "try to drop unit 1566 from 68 th HD.  usage down to 1.082097555347985\n",
      "try to drop unit 1566 from 85 th HD.  usage down to 1.069784465449485\n",
      "all done trying to reduce usage of unit 1566 final usage = 1.05799 231 sec elapsed 7 71 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00019 0.06621\n",
      "starting to reduce usage for unit 1481 with usage 1.0859 . Total units tried = 97\n",
      "try to drop unit 1481 from 21 th HD.  usage down to 1.0859041436761245\n",
      "try to drop unit 1481 from 42 th HD.  usage down to 1.0859041436761245\n",
      "try to drop unit 1481 from 63 th HD.  usage down to 1.0859041436761245\n",
      "try to drop unit 1481 from 84 th HD.  usage down to 1.0859041436761245\n",
      "try to drop unit 1481 from 105 th HD.  usage down to 1.0818096429871413\n",
      "all done trying to reduce usage of unit 1481 final usage = 1.08181 233 sec elapsed 1 89 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 1.00019 0.06619\n",
      "starting to reduce usage for unit 1315 with usage 1.08563 . Total units tried = 98\n",
      "try to drop unit 1315 from 13 th HD.  usage down to 1.0856264732569725\n",
      "try to drop unit 1315 from 26 th HD.  usage down to 1.0856264732569725\n",
      "try to drop unit 1315 from 39 th HD.  usage down to 1.0856264732569725\n",
      "try to drop unit 1315 from 52 th HD.  usage down to 1.0856264732569725\n",
      "try to drop unit 1315 from 65 th HD.  usage down to 1.0808901442220782\n",
      "all done trying to reduce usage of unit 1315 final usage = 1.08089 234 sec elapsed 1 16 successful, failed patches, couldn't start= 50\n",
      "current avg and SD of usage are 1.00019 0.06617\n",
      "starting to reduce usage for unit 1456 with usage 1.0852 . Total units tried = 99\n",
      "try to drop unit 1456 from 8 th HD.  usage down to 1.0852031396671127\n",
      "try to drop unit 1456 from 16 th HD.  usage down to 1.0852031396671127\n",
      "try to drop unit 1456 from 24 th HD.  usage down to 1.08266655210854\n",
      "try to drop unit 1456 from 32 th HD.  usage down to 1.08266655210854\n",
      "try to drop unit 1456 from 40 th HD.  usage down to 1.08266655210854\n",
      "all done trying to reduce usage of unit 1456 final usage = 1.08267 236 sec elapsed 1 40 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00019 0.06616\n",
      "starting to reduce usage for unit 1493 with usage 1.08508 . Total units tried = 100\n",
      "try to drop unit 1493 from 9 th HD.  usage down to 1.0850836503473966\n",
      "try to drop unit 1493 from 18 th HD.  usage down to 1.0850836503473966\n",
      "try to drop unit 1493 from 27 th HD.  usage down to 1.0850836503473966\n",
      "try to drop unit 1493 from 36 th HD.  usage down to 1.0850836503473966\n",
      "try to drop unit 1493 from 45 th HD.  usage down to 1.0850836503473966\n",
      "all done trying to reduce usage of unit 1493 final usage = 1.08133 238 sec elapsed 1 37 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00019 0.06613\n",
      "starting to reduce usage for unit 879 with usage 1.08489 . Total units tried = 101\n",
      "try to drop unit 879 from 11 th HD.  usage down to 1.084892467435847\n",
      "try to drop unit 879 from 22 th HD.  usage down to 1.084892467435847\n",
      "try to drop unit 879 from 33 th HD.  usage down to 1.084892467435847\n",
      "try to drop unit 879 from 44 th HD.  usage down to 1.084892467435847\n",
      "try to drop unit 879 from 55 th HD.  usage down to 1.084892467435847\n",
      "all done trying to reduce usage of unit 879 final usage = 1.08489 240 sec elapsed 0 45 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.00019 0.06613\n",
      "starting to reduce usage for unit 1384 with usage 1.08486 . Total units tried = 102\n",
      "try to drop unit 1384 from 20 th HD.  usage down to 1.0848640175978224\n",
      "try to drop unit 1384 from 40 th HD.  usage down to 1.0848640175978224\n",
      "try to drop unit 1384 from 60 th HD.  usage down to 1.0848640175978224\n",
      "try to drop unit 1384 from 80 th HD.  usage down to 1.0848640175978224\n",
      "try to drop unit 1384 from 100 th HD.  usage down to 1.0848640175978224\n",
      "all done trying to reduce usage of unit 1384 final usage = 1.08486 243 sec elapsed 0 90 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00019 0.06613\n",
      "starting to reduce usage for unit 1457 with usage 1.08467 . Total units tried = 103\n",
      "try to drop unit 1457 from 8 th HD.  usage down to 1.084667144718666\n",
      "try to drop unit 1457 from 16 th HD.  usage down to 1.084667144718666\n",
      "try to drop unit 1457 from 24 th HD.  usage down to 1.084667144718666\n",
      "try to drop unit 1457 from 32 th HD.  usage down to 1.084667144718666\n",
      "try to drop unit 1457 from 40 th HD.  usage down to 1.0740234913156468\n",
      "all done trying to reduce usage of unit 1457 final usage = 1.07033 246 sec elapsed 4 36 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00019 0.06607\n",
      "starting to reduce usage for unit 869 with usage 1.0839 . Total units tried = 104\n",
      "try to drop unit 869 from 12 th HD.  usage down to 1.083897861098391\n",
      "try to drop unit 869 from 24 th HD.  usage down to 1.083897861098391\n",
      "try to drop unit 869 from 36 th HD.  usage down to 1.083897861098391\n",
      "try to drop unit 869 from 48 th HD.  usage down to 1.083897861098391\n",
      "try to drop unit 869 from 60 th HD.  usage down to 1.083897861098391\n",
      "all done trying to reduce usage of unit 869 final usage = 1.0839 248 sec elapsed 0 47 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 1.00019 0.06607\n",
      "starting to reduce usage for unit 1298 with usage 1.08363 . Total units tried = 105\n",
      "try to drop unit 1298 from 16 th HD.  usage down to 1.0836338466014923\n",
      "try to drop unit 1298 from 32 th HD.  usage down to 1.0836338466014923\n",
      "try to drop unit 1298 from 48 th HD.  usage down to 1.0836338466014923\n",
      "try to drop unit 1298 from 64 th HD.  usage down to 1.0836338466014923\n",
      "try to drop unit 1298 from 80 th HD.  usage down to 1.0707221721108853\n",
      "all done trying to reduce usage of unit 1298 final usage = 1.07072 253 sec elapsed 3 71 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00019 0.06601\n",
      "starting to reduce usage for unit 1075 with usage 1.08277 . Total units tried = 106\n",
      "try to drop unit 1075 from 19 th HD.  usage down to 1.0827712475124776\n",
      "try to drop unit 1075 from 38 th HD.  usage down to 1.0827712475124776\n",
      "try to drop unit 1075 from 57 th HD.  usage down to 1.0794710663012343\n",
      "try to drop unit 1075 from 76 th HD.  usage down to 1.073325901287196\n",
      "try to drop unit 1075 from 95 th HD.  usage down to 1.0676734874678016\n",
      "all done trying to reduce usage of unit 1075 final usage = 1.06767 257 sec elapsed 5 67 successful, failed patches, couldn't start= 25\n",
      "current avg and SD of usage are 1.00019 0.06594\n",
      "starting to reduce usage for unit 1300 with usage 1.08242 . Total units tried = 107\n",
      "try to drop unit 1300 from 18 th HD.  usage down to 1.0824230214950226\n",
      "try to drop unit 1300 from 36 th HD.  usage down to 1.0824230214950226\n",
      "try to drop unit 1300 from 54 th HD.  usage down to 1.0824230214950226\n",
      "try to drop unit 1300 from 72 th HD.  usage down to 1.0824230214950226\n",
      "try to drop unit 1300 from 90 th HD.  usage down to 1.0780485743998443\n",
      "all done trying to reduce usage of unit 1300 final usage = 1.07514 260 sec elapsed 2 84 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00019 0.06589\n",
      "starting to reduce usage for unit 1304 with usage 1.08221 . Total units tried = 108\n",
      "try to drop unit 1304 from 11 th HD.  usage down to 1.0822124926936159\n",
      "try to drop unit 1304 from 22 th HD.  usage down to 1.0822124926936159\n",
      "try to drop unit 1304 from 33 th HD.  usage down to 1.0822124926936159\n",
      "try to drop unit 1304 from 44 th HD.  usage down to 1.0822124926936159\n",
      "try to drop unit 1304 from 55 th HD.  usage down to 1.0822124926936159\n",
      "all done trying to reduce usage of unit 1304 final usage = 1.08221 262 sec elapsed 0 53 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00019 0.06589\n",
      "starting to reduce usage for unit 867 with usage 1.08221 . Total units tried = 109\n",
      "try to drop unit 867 from 8 th HD.  usage down to 1.082206802726009\n",
      "try to drop unit 867 from 16 th HD.  usage down to 1.082206802726009\n",
      "try to drop unit 867 from 24 th HD.  usage down to 1.082206802726009\n",
      "try to drop unit 867 from 32 th HD.  usage down to 1.082206802726009\n",
      "try to drop unit 867 from 40 th HD.  usage down to 1.082206802726009\n",
      "all done trying to reduce usage of unit 867 final usage = 1.08221 263 sec elapsed 0 22 successful, failed patches, couldn't start= 20\n",
      "current avg and SD of usage are 1.00019 0.06589\n",
      "starting to reduce usage for unit 863 with usage 1.08215 . Total units tried = 110\n",
      "try to drop unit 863 from 9 th HD.  usage down to 1.0821544550240398\n",
      "try to drop unit 863 from 18 th HD.  usage down to 1.0821544550240398\n",
      "try to drop unit 863 from 27 th HD.  usage down to 1.0821544550240398\n",
      "try to drop unit 863 from 36 th HD.  usage down to 1.0821544550240398\n",
      "try to drop unit 863 from 45 th HD.  usage down to 1.0821544550240398\n",
      "all done trying to reduce usage of unit 863 final usage = 1.08215 264 sec elapsed 0 37 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.00019 0.06589\n",
      "starting to reduce usage for unit 1485 with usage 1.08168 . Total units tried = 111\n",
      "try to drop unit 1485 from 15 th HD.  usage down to 1.0816764977451683\n",
      "try to drop unit 1485 from 30 th HD.  usage down to 1.0816764977451683\n",
      "try to drop unit 1485 from 45 th HD.  usage down to 1.0816764977451683\n",
      "try to drop unit 1485 from 60 th HD.  usage down to 1.0816764977451683\n",
      "try to drop unit 1485 from 75 th HD.  usage down to 1.0687568572999124\n",
      "all done trying to reduce usage of unit 1485 final usage = 1.06542 268 sec elapsed 4 64 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00018 0.06581\n",
      "starting to reduce usage for unit 846 with usage 1.08112 . Total units tried = 112\n",
      "try to drop unit 846 from 7 th HD.  usage down to 1.081118880919821\n",
      "try to drop unit 846 from 14 th HD.  usage down to 1.081118880919821\n",
      "try to drop unit 846 from 21 th HD.  usage down to 1.081118880919821\n",
      "try to drop unit 846 from 28 th HD.  usage down to 1.081118880919821\n",
      "try to drop unit 846 from 35 th HD.  usage down to 1.081118880919821\n",
      "all done trying to reduce usage of unit 846 final usage = 1.08112 270 sec elapsed 0 32 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00018 0.06581\n",
      "starting to reduce usage for unit 1424 with usage 1.08099 . Total units tried = 113\n",
      "try to drop unit 1424 from 10 th HD.  usage down to 1.0809868736713681\n",
      "try to drop unit 1424 from 20 th HD.  usage down to 1.0809868736713681\n",
      "try to drop unit 1424 from 30 th HD.  usage down to 1.0809868736713681\n",
      "try to drop unit 1424 from 40 th HD.  usage down to 1.0809868736713681\n",
      "try to drop unit 1424 from 50 th HD.  usage down to 1.0809868736713681\n",
      "all done trying to reduce usage of unit 1424 final usage = 1.08099 272 sec elapsed 0 40 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 1.00018 0.06581\n",
      "starting to reduce usage for unit 1428 with usage 1.08067 . Total units tried = 114\n",
      "try to drop unit 1428 from 14 th HD.  usage down to 1.080673925453061\n",
      "try to drop unit 1428 from 28 th HD.  usage down to 1.080673925453061\n",
      "try to drop unit 1428 from 42 th HD.  usage down to 1.080673925453061\n",
      "try to drop unit 1428 from 56 th HD.  usage down to 1.080673925453061\n",
      "try to drop unit 1428 from 70 th HD.  usage down to 1.080673925453061\n",
      "all done trying to reduce usage of unit 1428 final usage = 1.07779 273 sec elapsed 1 51 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 1.00018 0.0658\n",
      "starting to reduce usage for unit 840 with usage 1.08065 . Total units tried = 115\n",
      "try to drop unit 840 from 9 th HD.  usage down to 1.0806545795632054\n",
      "try to drop unit 840 from 18 th HD.  usage down to 1.077180285343231\n",
      "try to drop unit 840 from 27 th HD.  usage down to 1.077180285343231\n",
      "try to drop unit 840 from 36 th HD.  usage down to 1.077180285343231\n",
      "try to drop unit 840 from 45 th HD.  usage down to 1.0734237687300199\n",
      "all done trying to reduce usage of unit 840 final usage = 1.07342 277 sec elapsed 2 43 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00018 0.06576\n",
      "starting to reduce usage for unit 880 with usage 1.08054 . Total units tried = 116\n",
      "try to drop unit 880 from 14 th HD.  usage down to 1.080543056198131\n",
      "try to drop unit 880 from 28 th HD.  usage down to 1.080543056198131\n",
      "try to drop unit 880 from 42 th HD.  usage down to 1.080543056198131\n",
      "try to drop unit 880 from 56 th HD.  usage down to 1.080543056198131\n",
      "try to drop unit 880 from 70 th HD.  usage down to 1.080543056198131\n",
      "all done trying to reduce usage of unit 880 final usage = 1.07612 278 sec elapsed 1 43 successful, failed patches, couldn't start= 29\n",
      "current avg and SD of usage are 1.00018 0.06572\n",
      "starting to reduce usage for unit 942 with usage 1.07943 . Total units tried = 117\n",
      "try to drop unit 942 from 8 th HD.  usage down to 1.0742852298255066\n",
      "try to drop unit 942 from 16 th HD.  usage down to 1.0742852298255066\n",
      "try to drop unit 942 from 24 th HD.  usage down to 1.0742852298255066\n",
      "try to drop unit 942 from 32 th HD.  usage down to 1.0742852298255066\n",
      "try to drop unit 942 from 40 th HD.  usage down to 1.0710874680311642\n",
      "all done trying to reduce usage of unit 942 final usage = 1.07109 281 sec elapsed 2 38 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00018 0.06569\n",
      "starting to reduce usage for unit 1380 with usage 1.07829 . Total units tried = 118\n",
      "try to drop unit 1380 from 16 th HD.  usage down to 1.0782909670198424\n",
      "try to drop unit 1380 from 32 th HD.  usage down to 1.0782909670198424\n",
      "try to drop unit 1380 from 48 th HD.  usage down to 1.073921071898748\n",
      "try to drop unit 1380 from 64 th HD.  usage down to 1.0621508049098216\n",
      "try to drop unit 1380 from 80 th HD.  usage down to 1.0575840369095737\n",
      "all done trying to reduce usage of unit 1380 final usage = 1.05758 284 sec elapsed 5 61 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 1.00018 0.06562\n",
      "starting to reduce usage for unit 1505 with usage 1.07741 . Total units tried = 119\n",
      "try to drop unit 1505 from 15 th HD.  usage down to 1.0774135740150583\n",
      "try to drop unit 1505 from 30 th HD.  usage down to 1.0774135740150583\n",
      "try to drop unit 1505 from 45 th HD.  usage down to 1.0774135740150583\n",
      "try to drop unit 1505 from 60 th HD.  usage down to 1.0774135740150583\n",
      "try to drop unit 1505 from 75 th HD.  usage down to 1.0774135740150583\n",
      "all done trying to reduce usage of unit 1505 final usage = 1.07741 285 sec elapsed 0 70 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00018 0.06562\n",
      "starting to reduce usage for unit 1569 with usage 1.07728 . Total units tried = 120\n",
      "try to drop unit 1569 from 19 th HD.  usage down to 1.07727929077957\n",
      "try to drop unit 1569 from 38 th HD.  usage down to 1.07727929077957\n",
      "try to drop unit 1569 from 57 th HD.  usage down to 1.07727929077957\n",
      "try to drop unit 1569 from 76 th HD.  usage down to 1.0733725590215708\n",
      "try to drop unit 1569 from 95 th HD.  usage down to 1.0497068457563956\n",
      "all done trying to reduce usage of unit 1569 final usage = 1.04447 293 sec elapsed 8 73 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 1.00018 0.06546\n",
      "starting to reduce usage for unit 859 with usage 1.077 . Total units tried = 121\n",
      "try to drop unit 859 from 9 th HD.  usage down to 1.0770027583539379\n",
      "try to drop unit 859 from 18 th HD.  usage down to 1.0770027583539379\n",
      "try to drop unit 859 from 27 th HD.  usage down to 1.0770027583539379\n",
      "try to drop unit 859 from 36 th HD.  usage down to 1.0770027583539379\n",
      "try to drop unit 859 from 45 th HD.  usage down to 1.0770027583539379\n",
      "all done trying to reduce usage of unit 859 final usage = 1.077 294 sec elapsed 0 35 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 1.00018 0.06546\n",
      "starting to reduce usage for unit 1054 with usage 1.07657 . Total units tried = 122\n",
      "try to drop unit 1054 from 14 th HD.  usage down to 1.0765748727899969\n",
      "try to drop unit 1054 from 28 th HD.  usage down to 1.0669440336207727\n",
      "try to drop unit 1054 from 42 th HD.  usage down to 1.0669440336207727\n",
      "try to drop unit 1054 from 56 th HD.  usage down to 1.0669440336207727\n",
      "try to drop unit 1054 from 70 th HD.  usage down to 1.0639067289129078\n",
      "all done trying to reduce usage of unit 1054 final usage = 1.06047 296 sec elapsed 5 54 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 1.00018 0.0654\n",
      "starting to reduce usage for unit 936 with usage 1.07649 . Total units tried = 123\n",
      "try to drop unit 936 from 8 th HD.  usage down to 1.076491799262955\n",
      "try to drop unit 936 from 16 th HD.  usage down to 1.076491799262955\n",
      "try to drop unit 936 from 24 th HD.  usage down to 1.076491799262955\n",
      "try to drop unit 936 from 32 th HD.  usage down to 1.076491799262955\n",
      "try to drop unit 936 from 40 th HD.  usage down to 1.076491799262955\n",
      "all done trying to reduce usage of unit 936 final usage = 1.07649 298 sec elapsed 0 43 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00018 0.0654\n",
      "starting to reduce usage for unit 1049 with usage 1.07592 . Total units tried = 124\n",
      "try to drop unit 1049 from 12 th HD.  usage down to 1.0759159745412676\n",
      "try to drop unit 1049 from 24 th HD.  usage down to 1.0759159745412676\n",
      "try to drop unit 1049 from 36 th HD.  usage down to 1.0759159745412676\n",
      "try to drop unit 1049 from 48 th HD.  usage down to 1.0759159745412676\n",
      "try to drop unit 1049 from 60 th HD.  usage down to 1.0759159745412676\n",
      "all done trying to reduce usage of unit 1049 final usage = 1.07592 301 sec elapsed 0 50 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 1.00018 0.0654\n",
      "starting to reduce usage for unit 1590 with usage 1.07578 . Total units tried = 125\n",
      "try to drop unit 1590 from 14 th HD.  usage down to 1.075778277325213\n",
      "try to drop unit 1590 from 28 th HD.  usage down to 1.075778277325213\n",
      "try to drop unit 1590 from 42 th HD.  usage down to 1.075778277325213\n",
      "try to drop unit 1590 from 56 th HD.  usage down to 1.075778277325213\n",
      "try to drop unit 1590 from 70 th HD.  usage down to 1.075778277325213\n",
      "all done trying to reduce usage of unit 1590 final usage = 1.07578 303 sec elapsed 0 55 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 1.00018 0.0654\n",
      "starting to reduce usage for unit 862 with usage 1.07577 . Total units tried = 126\n",
      "try to drop unit 862 from 6 th HD.  usage down to 1.075773725351129\n",
      "try to drop unit 862 from 12 th HD.  usage down to 1.075773725351129\n",
      "try to drop unit 862 from 18 th HD.  usage down to 1.075773725351129\n",
      "try to drop unit 862 from 24 th HD.  usage down to 1.075773725351129\n",
      "try to drop unit 862 from 30 th HD.  usage down to 1.075773725351129\n",
      "all done trying to reduce usage of unit 862 final usage = 1.07577 304 sec elapsed 0 29 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00018 0.0654\n",
      "starting to reduce usage for unit 878 with usage 1.0757 . Total units tried = 127\n",
      "try to drop unit 878 from 10 th HD.  usage down to 1.0756997557722519\n",
      "try to drop unit 878 from 20 th HD.  usage down to 1.0756997557722519\n",
      "try to drop unit 878 from 30 th HD.  usage down to 1.0756997557722519\n",
      "try to drop unit 878 from 40 th HD.  usage down to 1.0756997557722519\n",
      "try to drop unit 878 from 50 th HD.  usage down to 1.0756997557722519\n",
      "all done trying to reduce usage of unit 878 final usage = 1.06114 306 sec elapsed 4 36 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 1.00018 0.06534\n",
      "starting to reduce usage for unit 1184 with usage 1.07559 . Total units tried = 128\n",
      "try to drop unit 1184 from 13 th HD.  usage down to 1.075593922374792\n",
      "try to drop unit 1184 from 26 th HD.  usage down to 1.075593922374792\n",
      "try to drop unit 1184 from 39 th HD.  usage down to 1.075593922374792\n",
      "try to drop unit 1184 from 52 th HD.  usage down to 1.075593922374792\n",
      "try to drop unit 1184 from 65 th HD.  usage down to 1.0714197621393302\n",
      "all done trying to reduce usage of unit 1184 final usage = 1.06433 312 sec elapsed 3 60 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00018 0.06529\n",
      "starting to reduce usage for unit 1387 with usage 1.07555 . Total units tried = 129\n",
      "try to drop unit 1387 from 16 th HD.  usage down to 1.0755484026339506\n",
      "try to drop unit 1387 from 32 th HD.  usage down to 1.0755484026339506\n",
      "try to drop unit 1387 from 48 th HD.  usage down to 1.0755484026339506\n",
      "try to drop unit 1387 from 64 th HD.  usage down to 1.0638737271007985\n",
      "try to drop unit 1387 from 80 th HD.  usage down to 1.0638737271007985\n",
      "all done trying to reduce usage of unit 1387 final usage = 1.06387 316 sec elapsed 2 67 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 1.00018 0.06523\n",
      "starting to reduce usage for unit 908 with usage 1.09594 . Total units tried = 130\n",
      "try to drop unit 908 from 12 th HD.  usage down to 1.095940108538867\n",
      "try to drop unit 908 from 24 th HD.  usage down to 1.095940108538867\n",
      "try to drop unit 908 from 36 th HD.  usage down to 1.095940108538867\n",
      "try to drop unit 908 from 48 th HD.  usage down to 1.095940108538867\n",
      "try to drop unit 908 from 60 th HD.  usage down to 1.095940108538867\n",
      "all done trying to reduce usage of unit 908 final usage = 1.09594 318 sec elapsed 0 47 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 1.00018 0.06523\n",
      "starting to reduce usage for unit 1567 with usage 1.07534 . Total units tried = 131\n",
      "try to drop unit 1567 from 20 th HD.  usage down to 1.0753378738325425\n",
      "try to drop unit 1567 from 40 th HD.  usage down to 1.0753378738325425\n",
      "try to drop unit 1567 from 60 th HD.  usage down to 1.0753378738325425\n",
      "try to drop unit 1567 from 80 th HD.  usage down to 1.0753378738325425\n",
      "try to drop unit 1567 from 100 th HD.  usage down to 1.0544864185450922\n",
      "all done trying to reduce usage of unit 1567 final usage = 1.05449 325 sec elapsed 5 88 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00018 0.06514\n",
      "starting to reduce usage for unit 1154 with usage 1.07511 . Total units tried = 132\n",
      "try to drop unit 1154 from 9 th HD.  usage down to 1.0751136891088795\n",
      "try to drop unit 1154 from 18 th HD.  usage down to 1.0751136891088795\n",
      "try to drop unit 1154 from 27 th HD.  usage down to 1.0751136891088795\n",
      "try to drop unit 1154 from 36 th HD.  usage down to 1.0751136891088795\n",
      "try to drop unit 1154 from 45 th HD.  usage down to 1.0751136891088795\n",
      "all done trying to reduce usage of unit 1154 final usage = 1.07511 327 sec elapsed 0 46 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00018 0.06514\n",
      "starting to reduce usage for unit 1051 with usage 1.0749 . Total units tried = 133\n",
      "try to drop unit 1051 from 18 th HD.  usage down to 1.074903160307468\n",
      "try to drop unit 1051 from 36 th HD.  usage down to 1.074903160307468\n",
      "try to drop unit 1051 from 54 th HD.  usage down to 1.074903160307468\n",
      "try to drop unit 1051 from 72 th HD.  usage down to 1.0724780961139646\n",
      "try to drop unit 1051 from 90 th HD.  usage down to 1.0724780961139646\n",
      "all done trying to reduce usage of unit 1051 final usage = 1.07248 330 sec elapsed 1 79 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.00018 0.06512\n",
      "starting to reduce usage for unit 872 with usage 1.07437 . Total units tried = 134\n",
      "try to drop unit 872 from 7 th HD.  usage down to 1.0743671653590265\n",
      "try to drop unit 872 from 14 th HD.  usage down to 1.0743671653590265\n",
      "try to drop unit 872 from 21 th HD.  usage down to 1.0743671653590265\n",
      "try to drop unit 872 from 28 th HD.  usage down to 1.0743671653590265\n",
      "try to drop unit 872 from 35 th HD.  usage down to 1.0743671653590265\n",
      "all done trying to reduce usage of unit 872 final usage = 1.07437 332 sec elapsed 0 33 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00018 0.06512\n",
      "starting to reduce usage for unit 1191 with usage 1.07434 . Total units tried = 135\n",
      "try to drop unit 1191 from 16 th HD.  usage down to 1.0743409915080415\n",
      "try to drop unit 1191 from 32 th HD.  usage down to 1.0743409915080415\n",
      "try to drop unit 1191 from 48 th HD.  usage down to 1.0743409915080415\n",
      "try to drop unit 1191 from 64 th HD.  usage down to 1.0608580442698328\n",
      "try to drop unit 1191 from 80 th HD.  usage down to 1.0282055961683858\n",
      "all done trying to reduce usage of unit 1191 final usage = 1.02024 341 sec elapsed 13 53 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 1.00018 0.06489\n",
      "starting to reduce usage for unit 1484 with usage 1.074 . Total units tried = 136\n",
      "try to drop unit 1484 from 13 th HD.  usage down to 1.0740030074322657\n",
      "try to drop unit 1484 from 26 th HD.  usage down to 1.0740030074322657\n",
      "try to drop unit 1484 from 39 th HD.  usage down to 1.0740030074322657\n",
      "try to drop unit 1484 from 52 th HD.  usage down to 1.0740030074322657\n",
      "try to drop unit 1484 from 65 th HD.  usage down to 1.0710942959922873\n",
      "all done trying to reduce usage of unit 1484 final usage = 1.07109 343 sec elapsed 1 51 successful, failed patches, couldn't start= 18\n",
      "current avg and SD of usage are 1.00018 0.06488\n",
      "starting to reduce usage for unit 847 with usage 1.07371 . Total units tried = 137\n",
      "try to drop unit 847 from 5 th HD.  usage down to 1.073712819084382\n",
      "try to drop unit 847 from 10 th HD.  usage down to 1.073712819084382\n",
      "try to drop unit 847 from 15 th HD.  usage down to 1.073712819084382\n",
      "try to drop unit 847 from 20 th HD.  usage down to 1.073712819084382\n",
      "try to drop unit 847 from 25 th HD.  usage down to 1.073712819084382\n",
      "all done trying to reduce usage of unit 847 final usage = 1.07371 345 sec elapsed 0 25 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00018 0.06488\n",
      "starting to reduce usage for unit 1385 with usage 1.07359 . Total units tried = 138\n",
      "try to drop unit 1385 from 19 th HD.  usage down to 1.073586501803541\n",
      "try to drop unit 1385 from 38 th HD.  usage down to 1.073586501803541\n",
      "try to drop unit 1385 from 57 th HD.  usage down to 1.0698026733458228\n",
      "try to drop unit 1385 from 76 th HD.  usage down to 1.0698026733458228\n",
      "try to drop unit 1385 from 95 th HD.  usage down to 1.0698026733458228\n",
      "all done trying to reduce usage of unit 1385 final usage = 1.06549 348 sec elapsed 2 77 successful, failed patches, couldn't start= 20\n",
      "current avg and SD of usage are 1.00018 0.06483\n",
      "starting to reduce usage for unit 1573 with usage 1.08177 . Total units tried = 139\n",
      "try to drop unit 1573 from 16 th HD.  usage down to 1.0817698132139002\n",
      "try to drop unit 1573 from 32 th HD.  usage down to 1.0817698132139002\n",
      "try to drop unit 1573 from 48 th HD.  usage down to 1.0817698132139002\n",
      "try to drop unit 1573 from 64 th HD.  usage down to 1.0817698132139002\n",
      "try to drop unit 1573 from 80 th HD.  usage down to 1.0667892665018994\n",
      "all done trying to reduce usage of unit 1573 final usage = 1.06679 356 sec elapsed 4 71 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00018 0.06473\n",
      "starting to reduce usage for unit 1594 with usage 1.07351 . Total units tried = 140\n",
      "try to drop unit 1594 from 16 th HD.  usage down to 1.0735068422570648\n",
      "try to drop unit 1594 from 32 th HD.  usage down to 1.0735068422570648\n",
      "try to drop unit 1594 from 48 th HD.  usage down to 1.0735068422570648\n",
      "try to drop unit 1594 from 64 th HD.  usage down to 1.0735068422570648\n",
      "try to drop unit 1594 from 80 th HD.  usage down to 1.070970254698492\n",
      "all done trying to reduce usage of unit 1594 final usage = 1.06335 360 sec elapsed 3 74 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00018 0.06466\n",
      "starting to reduce usage for unit 897 with usage 1.07311 . Total units tried = 141\n",
      "try to drop unit 897 from 21 th HD.  usage down to 1.0731074065311312\n",
      "try to drop unit 897 from 42 th HD.  usage down to 1.0666538452728695\n",
      "try to drop unit 897 from 63 th HD.  usage down to 1.0636324724743007\n",
      "try to drop unit 897 from 84 th HD.  usage down to 1.0636324724743007\n",
      "try to drop unit 897 from 105 th HD.  usage down to 1.0636324724743007\n",
      "all done trying to reduce usage of unit 897 final usage = 1.06112 367 sec elapsed 3 68 successful, failed patches, couldn't start= 38\n",
      "current avg and SD of usage are 1.00017 0.06462\n",
      "starting to reduce usage for unit 1194 with usage 1.07257 . Total units tried = 142\n",
      "try to drop unit 1194 from 12 th HD.  usage down to 1.0725668596086169\n",
      "try to drop unit 1194 from 24 th HD.  usage down to 1.0725668596086169\n",
      "try to drop unit 1194 from 36 th HD.  usage down to 1.0725668596086169\n",
      "try to drop unit 1194 from 48 th HD.  usage down to 1.0725668596086169\n",
      "try to drop unit 1194 from 60 th HD.  usage down to 1.0691676729610369\n",
      "all done trying to reduce usage of unit 1194 final usage = 1.0645 370 sec elapsed 2 59 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00018 0.06458\n",
      "starting to reduce usage for unit 1228 with usage 1.0725 . Total units tried = 143\n",
      "try to drop unit 1228 from 17 th HD.  usage down to 1.0724951660167883\n",
      "try to drop unit 1228 from 34 th HD.  usage down to 1.06814120280499\n",
      "try to drop unit 1228 from 51 th HD.  usage down to 1.059617631331814\n",
      "try to drop unit 1228 from 68 th HD.  usage down to 1.0361100991660732\n",
      "all done trying to reduce usage of unit 1228 final usage = 1.01967 380 sec elapsed 13 50 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00017 0.06428\n",
      "starting to reduce usage for unit 1489 with usage 1.07155 . Total units tried = 144\n",
      "try to drop unit 1489 from 11 th HD.  usage down to 1.0715494934007377\n",
      "try to drop unit 1489 from 22 th HD.  usage down to 1.0715494934007377\n",
      "try to drop unit 1489 from 33 th HD.  usage down to 1.0677099032604849\n",
      "try to drop unit 1489 from 44 th HD.  usage down to 1.0632057249038984\n",
      "try to drop unit 1489 from 55 th HD.  usage down to 1.0590418066101264\n",
      "all done trying to reduce usage of unit 1489 final usage = 1.04585 386 sec elapsed 6 49 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00017 0.06411\n",
      "starting to reduce usage for unit 967 with usage 1.07149 . Total units tried = 145\n",
      "try to drop unit 967 from 21 th HD.  usage down to 1.0676973853317455\n",
      "try to drop unit 967 from 42 th HD.  usage down to 1.0658743197109144\n",
      "try to drop unit 967 from 63 th HD.  usage down to 1.0658743197109144\n",
      "try to drop unit 967 from 84 th HD.  usage down to 1.0524642040580567\n",
      "try to drop unit 967 from 105 th HD.  usage down to 1.050480681350748\n",
      "all done trying to reduce usage of unit 967 final usage = 1.05048 397 sec elapsed 6 77 successful, failed patches, couldn't start= 24\n",
      "current avg and SD of usage are 1.00017 0.06396\n",
      "starting to reduce usage for unit 1128 with usage 1.07146 . Total units tried = 146\n",
      "try to drop unit 1128 from 13 th HD.  usage down to 1.071458453919047\n",
      "try to drop unit 1128 from 26 th HD.  usage down to 1.071458453919047\n",
      "try to drop unit 1128 from 39 th HD.  usage down to 1.066895099899363\n",
      "try to drop unit 1128 from 52 th HD.  usage down to 1.0631977589492494\n",
      "try to drop unit 1128 from 65 th HD.  usage down to 1.059720050748712\n",
      "all done trying to reduce usage of unit 1128 final usage = 1.05603 405 sec elapsed 4 52 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00017 0.06381\n",
      "starting to reduce usage for unit 1585 with usage 1.07135 . Total units tried = 147\n",
      "try to drop unit 1585 from 15 th HD.  usage down to 1.0713526205215858\n",
      "try to drop unit 1585 from 30 th HD.  usage down to 1.0713526205215858\n",
      "try to drop unit 1585 from 45 th HD.  usage down to 1.0713526205215858\n",
      "try to drop unit 1585 from 60 th HD.  usage down to 1.0713526205215858\n",
      "try to drop unit 1585 from 75 th HD.  usage down to 1.0713526205215858\n",
      "all done trying to reduce usage of unit 1585 final usage = 1.07135 407 sec elapsed 0 69 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.00017 0.06381\n",
      "starting to reduce usage for unit 1373 with usage 1.07121 . Total units tried = 148\n",
      "try to drop unit 1373 from 21 th HD.  usage down to 1.0712058193573564\n",
      "try to drop unit 1373 from 42 th HD.  usage down to 1.0712058193573564\n",
      "try to drop unit 1373 from 63 th HD.  usage down to 1.067572206044426\n",
      "try to drop unit 1373 from 84 th HD.  usage down to 1.067572206044426\n",
      "try to drop unit 1373 from 105 th HD.  usage down to 1.0650549643757123\n",
      "all done trying to reduce usage of unit 1373 final usage = 1.06505 410 sec elapsed 2 81 successful, failed patches, couldn't start= 27\n",
      "current avg and SD of usage are 1.00017 0.06377\n",
      "starting to reduce usage for unit 1389 with usage 1.07119 . Total units tried = 149\n",
      "try to drop unit 1389 from 19 th HD.  usage down to 1.0711910254415877\n",
      "try to drop unit 1389 from 38 th HD.  usage down to 1.0711910254415877\n",
      "try to drop unit 1389 from 57 th HD.  usage down to 1.0711910254415877\n",
      "try to drop unit 1389 from 76 th HD.  usage down to 1.0711910254415877\n",
      "try to drop unit 1389 from 95 th HD.  usage down to 1.0711910254415877\n",
      "all done trying to reduce usage of unit 1389 final usage = 1.07119 413 sec elapsed 0 86 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.00017 0.06377\n",
      "starting to reduce usage for unit 1170 with usage 1.07074 . Total units tried = 150\n",
      "try to drop unit 1170 from 19 th HD.  usage down to 1.070741518000737\n",
      "try to drop unit 1170 from 38 th HD.  usage down to 1.070741518000737\n",
      "try to drop unit 1170 from 57 th HD.  usage down to 1.070741518000737\n",
      "try to drop unit 1170 from 76 th HD.  usage down to 1.070741518000737\n",
      "try to drop unit 1170 from 95 th HD.  usage down to 1.070741518000737\n",
      "all done trying to reduce usage of unit 1170 final usage = 1.07074 416 sec elapsed 0 67 successful, failed patches, couldn't start= 32\n",
      "current avg and SD of usage are 1.00017 0.06377\n",
      "starting to reduce usage for unit 985 with usage 1.07048 . Total units tried = 151\n",
      "try to drop unit 985 from 24 th HD.  usage down to 1.0704786414973613\n",
      "try to drop unit 985 from 48 th HD.  usage down to 1.0704786414973613\n",
      "try to drop unit 985 from 72 th HD.  usage down to 1.0704786414973613\n",
      "try to drop unit 985 from 96 th HD.  usage down to 1.0704786414973613\n",
      "try to drop unit 985 from 120 th HD.  usage down to 1.0704786414973613\n",
      "all done trying to reduce usage of unit 985 final usage = 1.07048 418 sec elapsed 0 82 successful, failed patches, couldn't start= 41\n",
      "current avg and SD of usage are 1.00017 0.06377\n",
      "starting to reduce usage for unit 1563 with usage 1.07044 . Total units tried = 152\n",
      "try to drop unit 1563 from 16 th HD.  usage down to 1.0704388117241252\n",
      "try to drop unit 1563 from 32 th HD.  usage down to 1.0704388117241252\n",
      "try to drop unit 1563 from 48 th HD.  usage down to 1.0704388117241252\n",
      "try to drop unit 1563 from 64 th HD.  usage down to 1.0704388117241252\n",
      "try to drop unit 1563 from 80 th HD.  usage down to 1.0546411856639606\n",
      "all done trying to reduce usage of unit 1563 final usage = 1.0424 421 sec elapsed 7 37 successful, failed patches, couldn't start= 38\n",
      "current avg and SD of usage are 1.00017 0.06363\n",
      "starting to reduce usage for unit 1483 with usage 1.07041 . Total units tried = 153\n",
      "try to drop unit 1483 from 19 th HD.  usage down to 1.0704058099120117\n",
      "try to drop unit 1483 from 38 th HD.  usage down to 1.0704058099120117\n",
      "try to drop unit 1483 from 57 th HD.  usage down to 1.0704058099120117\n",
      "try to drop unit 1483 from 76 th HD.  usage down to 1.0704058099120117\n",
      "try to drop unit 1483 from 95 th HD.  usage down to 1.0704058099120117\n",
      "all done trying to reduce usage of unit 1483 final usage = 1.07041 422 sec elapsed 0 65 successful, failed patches, couldn't start= 33\n",
      "current avg and SD of usage are 1.00017 0.06363\n",
      "starting to reduce usage for unit 1115 with usage 1.07025 . Total units tried = 154\n",
      "try to drop unit 1115 from 12 th HD.  usage down to 1.0702544567737062\n",
      "try to drop unit 1115 from 24 th HD.  usage down to 1.0702544567737062\n",
      "try to drop unit 1115 from 36 th HD.  usage down to 1.0702544567737062\n",
      "try to drop unit 1115 from 48 th HD.  usage down to 1.0702544567737062\n",
      "try to drop unit 1115 from 60 th HD.  usage down to 1.0702544567737062\n",
      "all done trying to reduce usage of unit 1115 final usage = 1.05911 425 sec elapsed 3 51 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00017 0.06356\n",
      "starting to reduce usage for unit 1195 with usage 1.07298 . Total units tried = 155\n",
      "try to drop unit 1195 from 15 th HD.  usage down to 1.0729765372762197\n",
      "try to drop unit 1195 from 30 th HD.  usage down to 1.0729765372762197\n",
      "try to drop unit 1195 from 45 th HD.  usage down to 1.0729765372762197\n",
      "try to drop unit 1195 from 60 th HD.  usage down to 1.0691927088185014\n",
      "try to drop unit 1195 from 75 th HD.  usage down to 1.0656535489678238\n",
      "all done trying to reduce usage of unit 1195 final usage = 1.05844 430 sec elapsed 4 69 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00017 0.06347\n",
      "starting to reduce usage for unit 1510 with usage 1.06958 . Total units tried = 156\n",
      "try to drop unit 1510 from 14 th HD.  usage down to 1.0695784886221609\n",
      "try to drop unit 1510 from 28 th HD.  usage down to 1.0695784886221609\n",
      "try to drop unit 1510 from 42 th HD.  usage down to 1.0695784886221609\n",
      "try to drop unit 1510 from 56 th HD.  usage down to 1.0695784886221609\n",
      "try to drop unit 1510 from 70 th HD.  usage down to 1.0695784886221609\n",
      "all done trying to reduce usage of unit 1510 final usage = 1.06958 432 sec elapsed 0 67 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00017 0.06347\n",
      "starting to reduce usage for unit 954 with usage 1.0695 . Total units tried = 157\n",
      "try to drop unit 954 from 7 th HD.  usage down to 1.0695033810497678\n",
      "try to drop unit 954 from 14 th HD.  usage down to 1.0695033810497678\n",
      "try to drop unit 954 from 21 th HD.  usage down to 1.0695033810497678\n",
      "try to drop unit 954 from 28 th HD.  usage down to 1.0695033810497678\n",
      "try to drop unit 954 from 35 th HD.  usage down to 1.0477005631786622\n",
      "all done trying to reduce usage of unit 954 final usage = 1.0477 436 sec elapsed 5 30 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00017 0.06324\n",
      "starting to reduce usage for unit 1165 with usage 1.06883 . Total units tried = 158\n",
      "try to drop unit 1165 from 19 th HD.  usage down to 1.0688285508917408\n",
      "try to drop unit 1165 from 38 th HD.  usage down to 1.0688285508917408\n",
      "try to drop unit 1165 from 57 th HD.  usage down to 1.0688285508917408\n",
      "try to drop unit 1165 from 76 th HD.  usage down to 1.0688285508917408\n",
      "try to drop unit 1165 from 95 th HD.  usage down to 1.062374989633479\n",
      "all done trying to reduce usage of unit 1165 final usage = 1.06237 440 sec elapsed 1 87 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00017 0.06322\n",
      "starting to reduce usage for unit 1241 with usage 1.06832 . Total units tried = 159\n",
      "try to drop unit 1241 from 17 th HD.  usage down to 1.0683175918007597\n",
      "try to drop unit 1241 from 34 th HD.  usage down to 1.0683175918007597\n",
      "try to drop unit 1241 from 51 th HD.  usage down to 1.0683175918007597\n",
      "try to drop unit 1241 from 68 th HD.  usage down to 1.0683175918007597\n",
      "try to drop unit 1241 from 85 th HD.  usage down to 1.065781004242187\n",
      "all done trying to reduce usage of unit 1241 final usage = 1.06175 444 sec elapsed 2 73 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00017 0.06318\n",
      "starting to reduce usage for unit 1035 with usage 1.06829 . Total units tried = 160\n",
      "try to drop unit 1035 from 18 th HD.  usage down to 1.0682902799562477\n",
      "try to drop unit 1035 from 36 th HD.  usage down to 1.0682902799562477\n",
      "try to drop unit 1035 from 54 th HD.  usage down to 1.0682902799562477\n",
      "try to drop unit 1035 from 72 th HD.  usage down to 1.0661736120069678\n",
      "try to drop unit 1035 from 90 th HD.  usage down to 1.0625047208948821\n",
      "all done trying to reduce usage of unit 1035 final usage = 1.0625 447 sec elapsed 2 55 successful, failed patches, couldn't start= 33\n",
      "current avg and SD of usage are 1.00017 0.06315\n",
      "starting to reduce usage for unit 1506 with usage 1.06793 . Total units tried = 161\n",
      "try to drop unit 1506 from 18 th HD.  usage down to 1.067926122029499\n",
      "try to drop unit 1506 from 36 th HD.  usage down to 1.067926122029499\n",
      "try to drop unit 1506 from 54 th HD.  usage down to 1.067926122029499\n",
      "try to drop unit 1506 from 72 th HD.  usage down to 1.067926122029499\n",
      "try to drop unit 1506 from 90 th HD.  usage down to 1.0545137303895993\n",
      "all done trying to reduce usage of unit 1506 final usage = 1.05036 451 sec elapsed 4 81 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00017 0.06306\n",
      "starting to reduce usage for unit 1542 with usage 1.06786 . Total units tried = 162\n",
      "try to drop unit 1542 from 17 th HD.  usage down to 1.0678635323858354\n",
      "try to drop unit 1542 from 34 th HD.  usage down to 1.0678635323858354\n",
      "try to drop unit 1542 from 51 th HD.  usage down to 1.0678635323858354\n",
      "try to drop unit 1542 from 68 th HD.  usage down to 1.0678635323858354\n",
      "try to drop unit 1542 from 85 th HD.  usage down to 1.0678635323858354\n",
      "all done trying to reduce usage of unit 1542 final usage = 1.06786 452 sec elapsed 0 65 successful, failed patches, couldn't start= 20\n",
      "current avg and SD of usage are 1.00017 0.06306\n",
      "starting to reduce usage for unit 1237 with usage 1.06771 . Total units tried = 163\n",
      "try to drop unit 1237 from 18 th HD.  usage down to 1.067705351286399\n",
      "try to drop unit 1237 from 36 th HD.  usage down to 1.067705351286399\n",
      "try to drop unit 1237 from 54 th HD.  usage down to 1.067705351286399\n",
      "try to drop unit 1237 from 72 th HD.  usage down to 1.067705351286399\n",
      "try to drop unit 1237 from 90 th HD.  usage down to 1.0647966398464206\n",
      "all done trying to reduce usage of unit 1237 final usage = 1.0648 455 sec elapsed 1 67 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 1.00017 0.06304\n",
      "starting to reduce usage for unit 1386 with usage 1.06763 . Total units tried = 164\n",
      "try to drop unit 1386 from 19 th HD.  usage down to 1.0676325197010503\n",
      "try to drop unit 1386 from 38 th HD.  usage down to 1.0676325197010503\n",
      "try to drop unit 1386 from 57 th HD.  usage down to 1.0676325197010503\n",
      "try to drop unit 1386 from 76 th HD.  usage down to 1.064723808261072\n",
      "try to drop unit 1386 from 95 th HD.  usage down to 1.064723808261072\n",
      "all done trying to reduce usage of unit 1386 final usage = 1.06472 459 sec elapsed 1 82 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 1.00016 0.06302\n",
      "starting to reduce usage for unit 1130 with usage 1.06805 . Total units tried = 165\n",
      "try to drop unit 1130 from 10 th HD.  usage down to 1.0680535773038635\n",
      "try to drop unit 1130 from 20 th HD.  usage down to 1.0680535773038635\n",
      "try to drop unit 1130 from 30 th HD.  usage down to 1.0680535773038635\n",
      "try to drop unit 1130 from 40 th HD.  usage down to 1.0680535773038635\n",
      "try to drop unit 1130 from 50 th HD.  usage down to 1.0680535773038635\n",
      "all done trying to reduce usage of unit 1130 final usage = 1.06407 461 sec elapsed 1 50 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00017 0.06298\n",
      "starting to reduce usage for unit 1494 with usage 1.06736 . Total units tried = 166\n",
      "try to drop unit 1494 from 9 th HD.  usage down to 1.0673559872754166\n",
      "try to drop unit 1494 from 18 th HD.  usage down to 1.0673559872754166\n",
      "try to drop unit 1494 from 27 th HD.  usage down to 1.0673559872754166\n",
      "try to drop unit 1494 from 36 th HD.  usage down to 1.0673559872754166\n",
      "try to drop unit 1494 from 45 th HD.  usage down to 1.0673559872754166\n",
      "all done trying to reduce usage of unit 1494 final usage = 1.06736 463 sec elapsed 0 43 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00017 0.06298\n",
      "starting to reduce usage for unit 968 with usage 1.06727 . Total units tried = 167\n",
      "try to drop unit 968 from 17 th HD.  usage down to 1.0672694997678094\n",
      "try to drop unit 968 from 34 th HD.  usage down to 1.0672694997678094\n",
      "try to drop unit 968 from 51 th HD.  usage down to 1.0672694997678094\n",
      "try to drop unit 968 from 68 th HD.  usage down to 1.0672694997678094\n",
      "try to drop unit 968 from 85 th HD.  usage down to 1.0672694997678094\n",
      "all done trying to reduce usage of unit 968 final usage = 1.06727 470 sec elapsed 0 46 successful, failed patches, couldn't start= 43\n",
      "current avg and SD of usage are 1.00017 0.06298\n",
      "starting to reduce usage for unit 861 with usage 1.06714 . Total units tried = 168\n",
      "try to drop unit 861 from 7 th HD.  usage down to 1.0671374925193609\n",
      "try to drop unit 861 from 14 th HD.  usage down to 1.0671374925193609\n",
      "try to drop unit 861 from 21 th HD.  usage down to 1.0671374925193609\n",
      "try to drop unit 861 from 28 th HD.  usage down to 1.0671374925193609\n",
      "try to drop unit 861 from 35 th HD.  usage down to 1.0671374925193609\n",
      "all done trying to reduce usage of unit 861 final usage = 1.06714 471 sec elapsed 0 28 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00017 0.06298\n",
      "starting to reduce usage for unit 1332 with usage 1.0669 . Total units tried = 169\n",
      "try to drop unit 1332 from 13 th HD.  usage down to 1.0668950998993634\n",
      "try to drop unit 1332 from 26 th HD.  usage down to 1.0668950998993634\n",
      "try to drop unit 1332 from 39 th HD.  usage down to 1.0668950998993634\n",
      "try to drop unit 1332 from 52 th HD.  usage down to 1.0668950998993634\n",
      "try to drop unit 1332 from 65 th HD.  usage down to 1.0668950998993634\n",
      "all done trying to reduce usage of unit 1332 final usage = 1.0669 474 sec elapsed 0 57 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00017 0.06298\n",
      "starting to reduce usage for unit 883 with usage 1.06614 . Total units tried = 170\n",
      "try to drop unit 883 from 10 th HD.  usage down to 1.0661406101948616\n",
      "try to drop unit 883 from 20 th HD.  usage down to 1.0661406101948616\n",
      "try to drop unit 883 from 30 th HD.  usage down to 1.0661406101948616\n",
      "try to drop unit 883 from 40 th HD.  usage down to 1.0661406101948616\n",
      "try to drop unit 883 from 50 th HD.  usage down to 1.0661406101948616\n",
      "all done trying to reduce usage of unit 883 final usage = 1.06614 475 sec elapsed 0 45 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00017 0.06298\n",
      "starting to reduce usage for unit 1491 with usage 1.06595 . Total units tried = 171\n",
      "try to drop unit 1491 from 13 th HD.  usage down to 1.0659482892897916\n",
      "try to drop unit 1491 from 26 th HD.  usage down to 1.0659482892897916\n",
      "try to drop unit 1491 from 39 th HD.  usage down to 1.0659482892897916\n",
      "try to drop unit 1491 from 52 th HD.  usage down to 1.0659482892897916\n",
      "try to drop unit 1491 from 65 th HD.  usage down to 1.0610355712591235\n",
      "all done trying to reduce usage of unit 1491 final usage = 1.0567 480 sec elapsed 2 61 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00017 0.06294\n",
      "starting to reduce usage for unit 981 with usage 1.0659 . Total units tried = 172\n",
      "try to drop unit 981 from 21 th HD.  usage down to 1.0621633228385432\n",
      "try to drop unit 981 from 42 th HD.  usage down to 1.0588153458994136\n",
      "try to drop unit 981 from 63 th HD.  usage down to 1.0511429935800336\n",
      "try to drop unit 981 from 84 th HD.  usage down to 1.0395354596646271\n",
      "all done trying to reduce usage of unit 981 final usage = 1.01772 498 sec elapsed 13 63 successful, failed patches, couldn't start= 21\n",
      "current avg and SD of usage are 1.00016 0.06233\n",
      "starting to reduce usage for unit 1604 with usage 1.06533 . Total units tried = 173\n",
      "try to drop unit 1604 from 14 th HD.  usage down to 1.0653280828207825\n",
      "try to drop unit 1604 from 28 th HD.  usage down to 1.0653280828207825\n",
      "try to drop unit 1604 from 42 th HD.  usage down to 1.0653280828207825\n",
      "try to drop unit 1604 from 56 th HD.  usage down to 1.0653280828207825\n",
      "try to drop unit 1604 from 70 th HD.  usage down to 1.0653280828207825\n",
      "all done trying to reduce usage of unit 1604 final usage = 1.06533 499 sec elapsed 0 63 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00016 0.06233\n",
      "starting to reduce usage for unit 987 with usage 1.06526 . Total units tried = 174\n",
      "try to drop unit 987 from 26 th HD.  usage down to 1.0609194959199675\n",
      "try to drop unit 987 from 52 th HD.  usage down to 1.0609194959199675\n",
      "try to drop unit 987 from 78 th HD.  usage down to 1.0486758236262572\n",
      "try to drop unit 987 from 104 th HD.  usage down to 1.0463827666812044\n",
      "try to drop unit 987 from 130 th HD.  usage down to 1.0426774597764437\n",
      "all done trying to reduce usage of unit 987 final usage = 1.04268 504 sec elapsed 7 55 successful, failed patches, couldn't start= 69\n",
      "current avg and SD of usage are 1.00016 0.06214\n",
      "starting to reduce usage for unit 1482 with usage 1.06488 . Total units tried = 175\n",
      "try to drop unit 1482 from 17 th HD.  usage down to 1.0648831273540251\n",
      "try to drop unit 1482 from 34 th HD.  usage down to 1.0648831273540251\n",
      "try to drop unit 1482 from 51 th HD.  usage down to 1.0648831273540251\n",
      "try to drop unit 1482 from 68 th HD.  usage down to 1.062213394553482\n",
      "try to drop unit 1482 from 85 th HD.  usage down to 1.0580608561949212\n",
      "all done trying to reduce usage of unit 1482 final usage = 1.05806 507 sec elapsed 2 68 successful, failed patches, couldn't start= 16\n",
      "current avg and SD of usage are 1.00017 0.0621\n",
      "starting to reduce usage for unit 1465 with usage 1.06477 . Total units tried = 176\n",
      "try to drop unit 1465 from 9 th HD.  usage down to 1.0647704659954331\n",
      "try to drop unit 1465 from 18 th HD.  usage down to 1.0647704659954331\n",
      "try to drop unit 1465 from 27 th HD.  usage down to 1.0647704659954331\n",
      "try to drop unit 1465 from 36 th HD.  usage down to 1.0647704659954331\n",
      "try to drop unit 1465 from 45 th HD.  usage down to 1.0647704659954331\n",
      "all done trying to reduce usage of unit 1465 final usage = 1.0611 509 sec elapsed 1 37 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00016 0.06207\n",
      "starting to reduce usage for unit 1378 with usage 1.06461 . Total units tried = 177\n",
      "try to drop unit 1378 from 19 th HD.  usage down to 1.0646134228895225\n",
      "try to drop unit 1378 from 38 th HD.  usage down to 1.0646134228895225\n",
      "try to drop unit 1378 from 57 th HD.  usage down to 1.0646134228895225\n",
      "try to drop unit 1378 from 76 th HD.  usage down to 1.0646134228895225\n",
      "try to drop unit 1378 from 95 th HD.  usage down to 1.0646134228895225\n",
      "all done trying to reduce usage of unit 1378 final usage = 1.06461 512 sec elapsed 0 73 successful, failed patches, couldn't start= 24\n",
      "current avg and SD of usage are 1.00016 0.06207\n",
      "starting to reduce usage for unit 1487 with usage 1.06451 . Total units tried = 178\n",
      "try to drop unit 1487 from 8 th HD.  usage down to 1.0645098654790985\n",
      "try to drop unit 1487 from 16 th HD.  usage down to 1.0645098654790985\n",
      "try to drop unit 1487 from 24 th HD.  usage down to 1.0645098654790985\n",
      "try to drop unit 1487 from 32 th HD.  usage down to 1.0645098654790985\n",
      "try to drop unit 1487 from 40 th HD.  usage down to 1.0645098654790985\n",
      "all done trying to reduce usage of unit 1487 final usage = 1.05414 516 sec elapsed 3 35 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00017 0.06198\n",
      "starting to reduce usage for unit 986 with usage 1.06416 . Total units tried = 179\n",
      "try to drop unit 986 from 17 th HD.  usage down to 1.0641582254810695\n",
      "try to drop unit 986 from 34 th HD.  usage down to 1.0641582254810695\n",
      "try to drop unit 986 from 51 th HD.  usage down to 1.0641582254810695\n",
      "try to drop unit 986 from 68 th HD.  usage down to 1.0641582254810695\n",
      "try to drop unit 986 from 85 th HD.  usage down to 1.0641582254810695\n",
      "all done trying to reduce usage of unit 986 final usage = 1.06416 520 sec elapsed 0 53 successful, failed patches, couldn't start= 33\n",
      "current avg and SD of usage are 1.00017 0.06198\n",
      "starting to reduce usage for unit 870 with usage 1.06361 . Total units tried = 180\n",
      "try to drop unit 870 from 5 th HD.  usage down to 1.063611988590939\n",
      "try to drop unit 870 from 10 th HD.  usage down to 1.063611988590939\n",
      "try to drop unit 870 from 15 th HD.  usage down to 1.063611988590939\n",
      "try to drop unit 870 from 20 th HD.  usage down to 1.063611988590939\n",
      "try to drop unit 870 from 25 th HD.  usage down to 1.063611988590939\n",
      "all done trying to reduce usage of unit 870 final usage = 1.06361 521 sec elapsed 0 20 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00017 0.06198\n",
      "starting to reduce usage for unit 1129 with usage 1.06346 . Total units tried = 181\n",
      "try to drop unit 1129 from 8 th HD.  usage down to 1.063461773446152\n",
      "try to drop unit 1129 from 16 th HD.  usage down to 1.063461773446152\n",
      "try to drop unit 1129 from 24 th HD.  usage down to 1.063461773446152\n",
      "try to drop unit 1129 from 32 th HD.  usage down to 1.063461773446152\n",
      "try to drop unit 1129 from 40 th HD.  usage down to 1.0596699790337856\n",
      "all done trying to reduce usage of unit 1129 final usage = 1.05967 523 sec elapsed 1 43 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00016 0.06196\n",
      "starting to reduce usage for unit 1243 with usage 1.06337 . Total units tried = 182\n",
      "try to drop unit 1243 from 15 th HD.  usage down to 1.063366181990382\n",
      "try to drop unit 1243 from 30 th HD.  usage down to 1.063366181990382\n",
      "try to drop unit 1243 from 45 th HD.  usage down to 1.063366181990382\n",
      "try to drop unit 1243 from 60 th HD.  usage down to 1.063366181990382\n",
      "try to drop unit 1243 from 75 th HD.  usage down to 1.0602025600016731\n",
      "all done trying to reduce usage of unit 1243 final usage = 1.0602 526 sec elapsed 1 59 successful, failed patches, couldn't start= 20\n",
      "current avg and SD of usage are 1.00016 0.06193\n",
      "starting to reduce usage for unit 980 with usage 1.06282 . Total units tried = 183\n",
      "try to drop unit 980 from 15 th HD.  usage down to 1.0628188071067153\n",
      "try to drop unit 980 from 30 th HD.  usage down to 1.0628188071067153\n",
      "try to drop unit 980 from 45 th HD.  usage down to 1.0628188071067153\n",
      "try to drop unit 980 from 60 th HD.  usage down to 1.0628188071067153\n",
      "try to drop unit 980 from 75 th HD.  usage down to 1.0591499159946296\n",
      "all done trying to reduce usage of unit 980 final usage = 1.05915 532 sec elapsed 1 46 successful, failed patches, couldn't start= 33\n",
      "current avg and SD of usage are 1.00017 0.06189\n",
      "starting to reduce usage for unit 1375 with usage 1.06263 . Total units tried = 184\n",
      "try to drop unit 1375 from 17 th HD.  usage down to 1.0626310381757345\n",
      "try to drop unit 1375 from 34 th HD.  usage down to 1.0626310381757345\n",
      "try to drop unit 1375 from 51 th HD.  usage down to 1.0626310381757345\n",
      "try to drop unit 1375 from 68 th HD.  usage down to 1.057409923900844\n",
      "try to drop unit 1375 from 85 th HD.  usage down to 1.054320271491008\n",
      "all done trying to reduce usage of unit 1375 final usage = 1.05432 537 sec elapsed 2 60 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 1.00017 0.06183\n",
      "starting to reduce usage for unit 1246 with usage 1.06259 . Total units tried = 185\n",
      "try to drop unit 1246 from 16 th HD.  usage down to 1.0625877944219277\n",
      "try to drop unit 1246 from 32 th HD.  usage down to 1.0625877944219277\n",
      "try to drop unit 1246 from 48 th HD.  usage down to 1.0625877944219277\n",
      "try to drop unit 1246 from 64 th HD.  usage down to 1.0588312778087166\n",
      "try to drop unit 1246 from 80 th HD.  usage down to 1.0521102880729918\n",
      "all done trying to reduce usage of unit 1246 final usage = 1.05211 542 sec elapsed 3 78 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00017 0.06178\n",
      "starting to reduce usage for unit 842 with usage 1.06234 . Total units tried = 186\n",
      "try to drop unit 842 from 8 th HD.  usage down to 1.0623408498278502\n",
      "try to drop unit 842 from 16 th HD.  usage down to 1.0623408498278502\n",
      "try to drop unit 842 from 24 th HD.  usage down to 1.0623408498278502\n",
      "try to drop unit 842 from 32 th HD.  usage down to 1.0623408498278502\n",
      "try to drop unit 842 from 40 th HD.  usage down to 1.0623408498278502\n",
      "all done trying to reduce usage of unit 842 final usage = 1.06234 544 sec elapsed 0 40 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00017 0.06178\n",
      "starting to reduce usage for unit 1242 with usage 1.06233 . Total units tried = 187\n",
      "try to drop unit 1242 from 21 th HD.  usage down to 1.0558747706408584\n",
      "try to drop unit 1242 from 42 th HD.  usage down to 1.0558747706408584\n",
      "try to drop unit 1242 from 63 th HD.  usage down to 1.05296605920088\n",
      "try to drop unit 1242 from 84 th HD.  usage down to 1.05296605920088\n",
      "try to drop unit 1242 from 105 th HD.  usage down to 1.04539498830488\n",
      "all done trying to reduce usage of unit 1242 final usage = 1.04539 548 sec elapsed 4 42 successful, failed patches, couldn't start= 61\n",
      "current avg and SD of usage are 1.00017 0.0617\n",
      "starting to reduce usage for unit 858 with usage 1.08015 . Total units tried = 188\n",
      "try to drop unit 858 from 8 th HD.  usage down to 1.0801481724463051\n",
      "try to drop unit 858 from 16 th HD.  usage down to 1.0801481724463051\n",
      "try to drop unit 858 from 24 th HD.  usage down to 1.0801481724463051\n",
      "try to drop unit 858 from 32 th HD.  usage down to 1.0801481724463051\n",
      "try to drop unit 858 from 40 th HD.  usage down to 1.0801481724463051\n",
      "all done trying to reduce usage of unit 858 final usage = 1.08015 549 sec elapsed 0 24 successful, failed patches, couldn't start= 21\n",
      "current avg and SD of usage are 1.00017 0.0617\n",
      "starting to reduce usage for unit 1591 with usage 1.06216 . Total units tried = 189\n",
      "try to drop unit 1591 from 15 th HD.  usage down to 1.062163322838553\n",
      "try to drop unit 1591 from 30 th HD.  usage down to 1.062163322838553\n",
      "try to drop unit 1591 from 45 th HD.  usage down to 1.062163322838553\n",
      "try to drop unit 1591 from 60 th HD.  usage down to 1.059007666804492\n",
      "try to drop unit 1591 from 75 th HD.  usage down to 1.059007666804492\n",
      "all done trying to reduce usage of unit 1591 final usage = 1.05901 554 sec elapsed 1 64 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 1.00017 0.06167\n",
      "starting to reduce usage for unit 1018 with usage 1.06198 . Total units tried = 190\n",
      "try to drop unit 1018 from 17 th HD.  usage down to 1.0619789678881244\n",
      "try to drop unit 1018 from 34 th HD.  usage down to 1.0619789678881244\n",
      "try to drop unit 1018 from 51 th HD.  usage down to 1.0619789678881244\n",
      "try to drop unit 1018 from 68 th HD.  usage down to 1.0582588670675883\n",
      "try to drop unit 1018 from 85 th HD.  usage down to 1.0520329045135506\n",
      "all done trying to reduce usage of unit 1018 final usage = 1.04863 563 sec elapsed 4 65 successful, failed patches, couldn't start= 18\n",
      "current avg and SD of usage are 1.00017 0.06154\n",
      "starting to reduce usage for unit 1359 with usage 1.06196 . Total units tried = 191\n",
      "try to drop unit 1359 from 20 th HD.  usage down to 1.0619562080177107\n",
      "try to drop unit 1359 from 40 th HD.  usage down to 1.047948645766266\n",
      "all done trying to reduce usage of unit 1359 final usage = 1.01896 569 sec elapsed 12 41 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00016 0.06131\n",
      "starting to reduce usage for unit 1197 with usage 1.06195 . Total units tried = 192\n",
      "try to drop unit 1197 from 13 th HD.  usage down to 1.0619527940371487\n",
      "try to drop unit 1197 from 26 th HD.  usage down to 1.0619527940371487\n",
      "try to drop unit 1197 from 39 th HD.  usage down to 1.0619527940371487\n",
      "try to drop unit 1197 from 52 th HD.  usage down to 1.0619527940371487\n",
      "try to drop unit 1197 from 65 th HD.  usage down to 1.0381072777956375\n",
      "all done trying to reduce usage of unit 1197 final usage = 1.02966 580 sec elapsed 8 52 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00016 0.06113\n",
      "starting to reduce usage for unit 1388 with usage 1.06156 . Total units tried = 193\n",
      "try to drop unit 1388 from 19 th HD.  usage down to 1.0615590482788417\n",
      "try to drop unit 1388 from 38 th HD.  usage down to 1.0615590482788417\n",
      "try to drop unit 1388 from 57 th HD.  usage down to 1.0571846011836634\n",
      "try to drop unit 1388 from 76 th HD.  usage down to 1.0571846011836634\n",
      "try to drop unit 1388 from 95 th HD.  usage down to 1.0571846011836634\n",
      "all done trying to reduce usage of unit 1388 final usage = 1.05718 584 sec elapsed 1 87 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00016 0.0611\n",
      "starting to reduce usage for unit 1572 with usage 1.06155 . Total units tried = 194\n",
      "try to drop unit 1572 from 16 th HD.  usage down to 1.061549944330673\n",
      "try to drop unit 1572 from 32 th HD.  usage down to 1.061549944330673\n",
      "try to drop unit 1572 from 48 th HD.  usage down to 1.061549944330673\n",
      "try to drop unit 1572 from 64 th HD.  usage down to 1.061549944330673\n",
      "try to drop unit 1572 from 80 th HD.  usage down to 1.0574167518619715\n",
      "all done trying to reduce usage of unit 1572 final usage = 1.05454 586 sec elapsed 2 68 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 1.00016 0.06106\n",
      "starting to reduce usage for unit 1458 with usage 1.06153 . Total units tried = 195\n",
      "try to drop unit 1458 from 13 th HD.  usage down to 1.0615260464667249\n",
      "try to drop unit 1458 from 26 th HD.  usage down to 1.0615260464667249\n",
      "try to drop unit 1458 from 39 th HD.  usage down to 1.0615260464667249\n",
      "try to drop unit 1458 from 52 th HD.  usage down to 1.0589075233746321\n",
      "try to drop unit 1458 from 65 th HD.  usage down to 1.053489536220588\n",
      "all done trying to reduce usage of unit 1458 final usage = 1.05349 590 sec elapsed 3 53 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 1.00016 0.06099\n",
      "starting to reduce usage for unit 944 with usage 1.06182 . Total units tried = 196\n",
      "try to drop unit 944 from 11 th HD.  usage down to 1.0618230627757388\n",
      "try to drop unit 944 from 22 th HD.  usage down to 1.0618230627757388\n",
      "try to drop unit 944 from 33 th HD.  usage down to 1.0618230627757388\n",
      "try to drop unit 944 from 44 th HD.  usage down to 1.057405371926758\n",
      "try to drop unit 944 from 55 th HD.  usage down to 1.049218646535833\n",
      "all done trying to reduce usage of unit 944 final usage = 1.03368 593 sec elapsed 8 44 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00016 0.06084\n",
      "starting to reduce usage for unit 1235 with usage 1.06123 . Total units tried = 197\n",
      "try to drop unit 1235 from 21 th HD.  usage down to 1.06123472012532\n",
      "try to drop unit 1235 from 42 th HD.  usage down to 1.06123472012532\n",
      "try to drop unit 1235 from 63 th HD.  usage down to 1.06123472012532\n",
      "try to drop unit 1235 from 84 th HD.  usage down to 1.0575658290132344\n",
      "try to drop unit 1235 from 105 th HD.  usage down to 1.0575658290132344\n",
      "all done trying to reduce usage of unit 1235 final usage = 1.05757 598 sec elapsed 1 85 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 1.00015 0.06081\n",
      "starting to reduce usage for unit 1073 with usage 1.06107 . Total units tried = 198\n",
      "try to drop unit 1073 from 15 th HD.  usage down to 1.0610651590906737\n",
      "try to drop unit 1073 from 30 th HD.  usage down to 1.0610651590906737\n",
      "try to drop unit 1073 from 45 th HD.  usage down to 1.0610651590906737\n",
      "try to drop unit 1073 from 60 th HD.  usage down to 1.0610651590906737\n",
      "try to drop unit 1073 from 75 th HD.  usage down to 1.0506206545538503\n",
      "all done trying to reduce usage of unit 1073 final usage = 1.05062 603 sec elapsed 3 68 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00015 0.06073\n",
      "starting to reduce usage for unit 1330 with usage 1.06086 . Total units tried = 199\n",
      "try to drop unit 1330 from 13 th HD.  usage down to 1.0608637342374359\n",
      "try to drop unit 1330 from 26 th HD.  usage down to 1.0608637342374359\n",
      "try to drop unit 1330 from 39 th HD.  usage down to 1.0608637342374359\n",
      "try to drop unit 1330 from 52 th HD.  usage down to 1.0608637342374359\n",
      "try to drop unit 1330 from 65 th HD.  usage down to 1.0568602730301415\n",
      "all done trying to reduce usage of unit 1330 final usage = 1.05686 604 sec elapsed 1 53 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.00015 0.0607\n",
      "starting to reduce usage for unit 839 with usage 1.06064 . Total units tried = 200\n",
      "try to drop unit 839 from 10 th HD.  usage down to 1.0606441014878625\n",
      "try to drop unit 839 from 20 th HD.  usage down to 1.0606441014878625\n",
      "try to drop unit 839 from 30 th HD.  usage down to 1.0606441014878625\n",
      "try to drop unit 839 from 40 th HD.  usage down to 1.0606441014878625\n",
      "try to drop unit 839 from 50 th HD.  usage down to 1.0606441014878625\n",
      "all done trying to reduce usage of unit 839 final usage = 1.06064 608 sec elapsed 0 45 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00015 0.0607\n",
      "starting to reduce usage for unit 1461 with usage 1.06013 . Total units tried = 201\n",
      "try to drop unit 1461 from 11 th HD.  usage down to 1.0601320044033578\n",
      "try to drop unit 1461 from 22 th HD.  usage down to 1.0601320044033578\n",
      "try to drop unit 1461 from 33 th HD.  usage down to 1.0601320044033578\n",
      "try to drop unit 1461 from 44 th HD.  usage down to 1.0601320044033578\n",
      "try to drop unit 1461 from 55 th HD.  usage down to 1.0601320044033578\n",
      "all done trying to reduce usage of unit 1461 final usage = 1.06013 609 sec elapsed 0 55 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00015 0.0607\n",
      "starting to reduce usage for unit 946 with usage 1.06104 . Total units tried = 202\n",
      "try to drop unit 946 from 10 th HD.  usage down to 1.0610435372137743\n",
      "try to drop unit 946 from 20 th HD.  usage down to 1.0610435372137743\n",
      "try to drop unit 946 from 30 th HD.  usage down to 1.0610435372137743\n",
      "try to drop unit 946 from 40 th HD.  usage down to 1.0610435372137743\n",
      "try to drop unit 946 from 50 th HD.  usage down to 1.0610435372137743\n",
      "all done trying to reduce usage of unit 946 final usage = 1.05706 613 sec elapsed 1 47 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00015 0.06065\n",
      "starting to reduce usage for unit 841 with usage 1.05992 . Total units tried = 203\n",
      "try to drop unit 841 from 12 th HD.  usage down to 1.0599237515889943\n",
      "try to drop unit 841 from 24 th HD.  usage down to 1.0599237515889943\n",
      "try to drop unit 841 from 36 th HD.  usage down to 1.0599237515889943\n",
      "try to drop unit 841 from 48 th HD.  usage down to 1.0563982476605702\n",
      "try to drop unit 841 from 60 th HD.  usage down to 1.0563982476605702\n",
      "all done trying to reduce usage of unit 841 final usage = 1.0564 617 sec elapsed 1 50 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00015 0.06061\n",
      "starting to reduce usage for unit 860 with usage 1.05988 . Total units tried = 204\n",
      "try to drop unit 860 from 7 th HD.  usage down to 1.0598827838222336\n",
      "try to drop unit 860 from 14 th HD.  usage down to 1.0598827838222336\n",
      "try to drop unit 860 from 21 th HD.  usage down to 1.0598827838222336\n",
      "try to drop unit 860 from 28 th HD.  usage down to 1.0598827838222336\n",
      "try to drop unit 860 from 35 th HD.  usage down to 1.0598827838222336\n",
      "all done trying to reduce usage of unit 860 final usage = 1.05988 619 sec elapsed 0 31 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00015 0.06061\n",
      "starting to reduce usage for unit 1589 with usage 1.0596 . Total units tried = 205\n",
      "try to drop unit 1589 from 12 th HD.  usage down to 1.059598285441953\n",
      "try to drop unit 1589 from 24 th HD.  usage down to 1.059598285441953\n",
      "try to drop unit 1589 from 36 th HD.  usage down to 1.059598285441953\n",
      "try to drop unit 1589 from 48 th HD.  usage down to 1.059598285441953\n",
      "try to drop unit 1589 from 60 th HD.  usage down to 1.059598285441953\n",
      "all done trying to reduce usage of unit 1589 final usage = 1.0596 620 sec elapsed 0 55 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00015 0.06061\n",
      "starting to reduce usage for unit 1226 with usage 1.05933 . Total units tried = 206\n",
      "try to drop unit 1226 from 14 th HD.  usage down to 1.0593297189709678\n",
      "try to drop unit 1226 from 28 th HD.  usage down to 1.0570139021554927\n",
      "try to drop unit 1226 from 42 th HD.  usage down to 1.0570139021554927\n",
      "try to drop unit 1226 from 56 th HD.  usage down to 1.0524471341552448\n",
      "try to drop unit 1226 from 70 th HD.  usage down to 1.040454958429701\n",
      "all done trying to reduce usage of unit 1226 final usage = 1.04045 625 sec elapsed 5 55 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 1.00015 0.06051\n",
      "starting to reduce usage for unit 1125 with usage 1.05842 . Total units tried = 207\n",
      "try to drop unit 1125 from 10 th HD.  usage down to 1.05842387612816\n",
      "try to drop unit 1125 from 20 th HD.  usage down to 1.05842387612816\n",
      "try to drop unit 1125 from 30 th HD.  usage down to 1.05842387612816\n",
      "try to drop unit 1125 from 40 th HD.  usage down to 1.05842387612816\n",
      "try to drop unit 1125 from 50 th HD.  usage down to 1.05842387612816\n",
      "all done trying to reduce usage of unit 1125 final usage = 1.05058 628 sec elapsed 2 39 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.00015 0.06045\n",
      "starting to reduce usage for unit 1055 with usage 1.05841 . Total units tried = 208\n",
      "try to drop unit 1055 from 12 th HD.  usage down to 1.058409082212385\n",
      "try to drop unit 1055 from 24 th HD.  usage down to 1.058409082212385\n",
      "try to drop unit 1055 from 36 th HD.  usage down to 1.058409082212385\n",
      "try to drop unit 1055 from 48 th HD.  usage down to 1.058409082212385\n",
      "try to drop unit 1055 from 60 th HD.  usage down to 1.0545888379619908\n",
      "all done trying to reduce usage of unit 1055 final usage = 1.04702 632 sec elapsed 3 49 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00015 0.06035\n",
      "starting to reduce usage for unit 1314 with usage 1.0583 . Total units tried = 209\n",
      "try to drop unit 1314 from 12 th HD.  usage down to 1.0583032488149215\n",
      "try to drop unit 1314 from 24 th HD.  usage down to 1.0583032488149215\n",
      "try to drop unit 1314 from 36 th HD.  usage down to 1.055911186433531\n",
      "try to drop unit 1314 from 48 th HD.  usage down to 1.055911186433531\n",
      "try to drop unit 1314 from 60 th HD.  usage down to 1.055911186433531\n",
      "all done trying to reduce usage of unit 1314 final usage = 1.05256 637 sec elapsed 2 9 successful, failed patches, couldn't start= 53\n",
      "current avg and SD of usage are 1.00015 0.06031\n",
      "starting to reduce usage for unit 857 with usage 1.05813 . Total units tried = 210\n",
      "try to drop unit 857 from 9 th HD.  usage down to 1.0581325497867549\n",
      "try to drop unit 857 from 18 th HD.  usage down to 1.0581325497867549\n",
      "try to drop unit 857 from 27 th HD.  usage down to 1.0581325497867549\n",
      "try to drop unit 857 from 36 th HD.  usage down to 1.0581325497867549\n",
      "try to drop unit 857 from 45 th HD.  usage down to 1.0581325497867549\n",
      "all done trying to reduce usage of unit 857 final usage = 1.05813 639 sec elapsed 0 30 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 1.00015 0.06031\n",
      "starting to reduce usage for unit 1575 with usage 1.05813 . Total units tried = 211\n",
      "try to drop unit 1575 from 9 th HD.  usage down to 1.0581268598191513\n",
      "try to drop unit 1575 from 18 th HD.  usage down to 1.0581268598191513\n",
      "try to drop unit 1575 from 27 th HD.  usage down to 1.0581268598191513\n",
      "try to drop unit 1575 from 36 th HD.  usage down to 1.0581268598191513\n",
      "try to drop unit 1575 from 45 th HD.  usage down to 1.0581268598191513\n",
      "all done trying to reduce usage of unit 1575 final usage = 1.05051 644 sec elapsed 2 45 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00015 0.06026\n",
      "starting to reduce usage for unit 945 with usage 1.05805 . Total units tried = 212\n",
      "try to drop unit 945 from 11 th HD.  usage down to 1.0580494762597081\n",
      "try to drop unit 945 from 22 th HD.  usage down to 1.0528283619848178\n",
      "try to drop unit 945 from 33 th HD.  usage down to 1.0528283619848178\n",
      "try to drop unit 945 from 44 th HD.  usage down to 1.0528283619848178\n",
      "try to drop unit 945 from 55 th HD.  usage down to 1.0528283619848178\n",
      "all done trying to reduce usage of unit 945 final usage = 1.05283 646 sec elapsed 1 46 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 1.00015 0.06023\n",
      "starting to reduce usage for unit 1053 with usage 1.05799 . Total units tried = 213\n",
      "try to drop unit 1053 from 11 th HD.  usage down to 1.0579903005966143\n",
      "try to drop unit 1053 from 22 th HD.  usage down to 1.0579903005966143\n",
      "try to drop unit 1053 from 33 th HD.  usage down to 1.0579903005966143\n",
      "try to drop unit 1053 from 44 th HD.  usage down to 1.0579903005966143\n",
      "try to drop unit 1053 from 55 th HD.  usage down to 1.0579903005966143\n",
      "all done trying to reduce usage of unit 1053 final usage = 1.05024 649 sec elapsed 2 49 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00015 0.06016\n",
      "starting to reduce usage for unit 1297 with usage 1.05768 . Total units tried = 214\n",
      "try to drop unit 1297 from 20 th HD.  usage down to 1.057675076391265\n",
      "try to drop unit 1297 from 40 th HD.  usage down to 1.057675076391265\n",
      "try to drop unit 1297 from 60 th HD.  usage down to 1.057675076391265\n",
      "try to drop unit 1297 from 80 th HD.  usage down to 1.057675076391265\n",
      "try to drop unit 1297 from 100 th HD.  usage down to 1.057675076391265\n",
      "all done trying to reduce usage of unit 1297 final usage = 1.05768 652 sec elapsed 0 94 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00015 0.06016\n",
      "starting to reduce usage for unit 1247 with usage 1.05757 . Total units tried = 215\n",
      "try to drop unit 1247 from 17 th HD.  usage down to 1.0575681050002843\n",
      "try to drop unit 1247 from 34 th HD.  usage down to 1.0575681050002843\n",
      "try to drop unit 1247 from 51 th HD.  usage down to 1.0575681050002843\n",
      "try to drop unit 1247 from 68 th HD.  usage down to 1.0575681050002843\n",
      "try to drop unit 1247 from 85 th HD.  usage down to 1.0575681050002843\n",
      "all done trying to reduce usage of unit 1247 final usage = 1.05757 655 sec elapsed 0 56 successful, failed patches, couldn't start= 31\n",
      "current avg and SD of usage are 1.00015 0.06016\n",
      "starting to reduce usage for unit 1122 with usage 1.05749 . Total units tried = 216\n",
      "try to drop unit 1122 from 11 th HD.  usage down to 1.0574861694667583\n",
      "try to drop unit 1122 from 22 th HD.  usage down to 1.0574861694667583\n",
      "try to drop unit 1122 from 33 th HD.  usage down to 1.0574861694667583\n",
      "try to drop unit 1122 from 44 th HD.  usage down to 1.0574861694667583\n",
      "try to drop unit 1122 from 55 th HD.  usage down to 1.053694375054392\n",
      "all done trying to reduce usage of unit 1122 final usage = 1.05369 657 sec elapsed 1 52 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00015 0.06014\n",
      "starting to reduce usage for unit 1273 with usage 1.05693 . Total units tried = 217\n",
      "try to drop unit 1273 from 13 th HD.  usage down to 1.0569285526414094\n",
      "try to drop unit 1273 from 26 th HD.  usage down to 1.0569285526414094\n",
      "try to drop unit 1273 from 39 th HD.  usage down to 1.0569285526414094\n",
      "try to drop unit 1273 from 52 th HD.  usage down to 1.0569285526414094\n",
      "try to drop unit 1273 from 65 th HD.  usage down to 1.0569285526414094\n",
      "all done trying to reduce usage of unit 1273 final usage = 1.04754 660 sec elapsed 2 57 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.00015 0.06009\n",
      "starting to reduce usage for unit 1301 with usage 1.05664 . Total units tried = 218\n",
      "try to drop unit 1301 from 18 th HD.  usage down to 1.0566395022870476\n",
      "try to drop unit 1301 from 36 th HD.  usage down to 1.0566395022870476\n",
      "try to drop unit 1301 from 54 th HD.  usage down to 1.0566395022870476\n",
      "try to drop unit 1301 from 72 th HD.  usage down to 1.0566395022870476\n",
      "try to drop unit 1301 from 90 th HD.  usage down to 1.0566395022870476\n",
      "all done trying to reduce usage of unit 1301 final usage = 1.05664 662 sec elapsed 0 75 successful, failed patches, couldn't start= 16\n",
      "current avg and SD of usage are 1.00015 0.06009\n",
      "starting to reduce usage for unit 1592 with usage 1.05529 . Total units tried = 219\n",
      "try to drop unit 1592 from 15 th HD.  usage down to 1.0552864279904366\n",
      "try to drop unit 1592 from 30 th HD.  usage down to 1.0552864279904366\n",
      "try to drop unit 1592 from 45 th HD.  usage down to 1.0552864279904366\n",
      "try to drop unit 1592 from 60 th HD.  usage down to 1.0552864279904366\n",
      "try to drop unit 1592 from 75 th HD.  usage down to 1.0552864279904366\n",
      "all done trying to reduce usage of unit 1592 final usage = 1.05529 664 sec elapsed 0 74 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00015 0.06009\n",
      "starting to reduce usage for unit 843 with usage 1.05507 . Total units tried = 220\n",
      "try to drop unit 843 from 6 th HD.  usage down to 1.0528135680690482\n",
      "try to drop unit 843 from 12 th HD.  usage down to 1.0528135680690482\n",
      "try to drop unit 843 from 18 th HD.  usage down to 1.0528135680690482\n",
      "try to drop unit 843 from 24 th HD.  usage down to 1.0528135680690482\n",
      "try to drop unit 843 from 30 th HD.  usage down to 1.0496158062747059\n",
      "all done trying to reduce usage of unit 843 final usage = 1.04549 669 sec elapsed 3 21 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00015 0.06004\n",
      "starting to reduce usage for unit 864 with usage 1.05501 . Total units tried = 221\n",
      "try to drop unit 864 from 4 th HD.  usage down to 1.0550098955648055\n",
      "try to drop unit 864 from 8 th HD.  usage down to 1.0550098955648055\n",
      "try to drop unit 864 from 12 th HD.  usage down to 1.0550098955648055\n",
      "try to drop unit 864 from 16 th HD.  usage down to 1.0550098955648055\n",
      "try to drop unit 864 from 20 th HD.  usage down to 1.0550098955648055\n",
      "try to drop unit 864 from 24 th HD.  usage down to 1.0550098955648055\n",
      "all done trying to reduce usage of unit 864 final usage = 1.05501 671 sec elapsed 0 17 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00015 0.06004\n",
      "starting to reduce usage for unit 1186 with usage 1.05448 . Total units tried = 222\n",
      "try to drop unit 1186 from 15 th HD.  usage down to 1.05448414255805\n",
      "try to drop unit 1186 from 30 th HD.  usage down to 1.05448414255805\n",
      "try to drop unit 1186 from 45 th HD.  usage down to 1.05448414255805\n",
      "try to drop unit 1186 from 60 th HD.  usage down to 1.05448414255805\n",
      "try to drop unit 1186 from 75 th HD.  usage down to 1.0273475490534636\n",
      "all done trying to reduce usage of unit 1186 final usage = 1.01825 678 sec elapsed 8 65 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00015 0.05987\n"
     ]
    }
   ],
   "source": [
    "#SECOND PATCHING BLOCK -- OVERUSERS.  applies a suppression of LONG STRINGS.  run first and third for MA\n",
    "maxSD = 0.06 #0.06  #0.08\n",
    "stopMaxUse = 1.+  2.* maxSD  #0.5*maxSD  #don't be too aggressive; may create long chains\n",
    "print(\"default use threshold for moving on to next overused unit is\",r5(stopMaxUse) )\n",
    "stopMaxUse = float(input(\"enter updated stopMaxUse value\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage above\",stopMaxUse)\n",
    "nBigUsers = 5\n",
    "maxExchangePop = 0.05*aDP\n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP \n",
    "print(\"this block reduces overuse, with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        maxNtries +=1\n",
    "#maxNtries = int(currSD * nUnits / nDistricts)   #try up to about 10% of the districts a unit is drawn into\n",
    "#maxNtries = 10 #overrirde\n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "attemptedBigUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to reduce up to\",maxNtries,\"units' overusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedBigUs) < maxNtries: \n",
    "    #each round, find the (5) most overused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nBigFound, bigUsers = 0,0,list()\n",
    "    while nBigFound < nBigUsers:\n",
    "        consideredBigU = idx[-idxNo-1]\n",
    "        if consideredBigU not in attemptedBigUs:\n",
    "            bigUsers.append(consideredBigU)\n",
    "            nBigFound +=1\n",
    "        idxNo +=1\n",
    "    OUUclusters = [ [b] + unitNbrs[b] for b in bigUsers ]\n",
    "    bigOverUse = [np.sum([(unitUse[j] - 1.) for j in OUUclusters[i] ]) for i in range(nBigUsers) ]\n",
    "    bigI = bigOverUse.index(np.max(bigOverUse))\n",
    "    OUU = bigUsers[ bigI ]  #pick the unit that centers cluster with most overuse\n",
    "    attemptedBigUs.append(OUU)  #so we don't try this unit again in a future loop\n",
    "    OUUc = OUUclusters[bigI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    print(\"starting to reduce usage for unit\",OUU,\"with usage\",r5(unitUse[OUU]),\". Total units tried =\",len(attemptedBigUs) )\n",
    "    for u in OUUc.copy():\n",
    "        if unitUse[u] < 1.:\n",
    "            OUUc.remove(u)  #...drop any underused neighbors of the primary OUU from the target sheddable cluster\n",
    "    OUU2nbrSet = set( unitNbrs[OUU])\n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.union(set(unitNbrs[u])) \n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.difference({u})\n",
    "    ouuHDs, ouuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if OUU in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(OUU2nbrSet) ) > 0: #the OUU or one of its overused 1-neighbors adjoins the complement\n",
    "                ouuHDs.append(t)  #so we can shed the OOUc to the complement\n",
    "                ouuDists.append( unitCP[OUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo, idx0 = 0, 0, 0, 0, np.argsort(ouuDists)\n",
    "    while unitUse[OUU] > stopMaxUse and idxNo > -0.5*len(ouuHDs): #arbitrarily only examine 50% of HDs, biasing farthest ones\n",
    "        idxNo -=1\n",
    "        if idxNo % int(0.1*len(ouuHDs)) == 0:\n",
    "            print(\"try to drop unit\",OUU,\"from\",abs(idxNo),\"th HD.  usage down to\",unitUse[OUU] )\n",
    "        t = ouuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).difference(set(OUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        if not (contig and complementContig):  #dropping the cluster creates a problem (likely an HD discontig).  Try something simpler ...\n",
    "            if len(set(unitNbrs[OUU]).intersection(HDadjoinSet) )  > 0:  #Yay! The OUU itself on border.  Try dropping just it, not the full cluster\n",
    "                HDouuSet = {OUU}\n",
    "                trySet = set(HDunitList[t]).difference( {OUU} )\n",
    "                contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        else:\n",
    "            HDouuSet = set(HDunitList[t]).intersection(set(OUUc))\n",
    "        if contig and complementContig:  #we can at least drop the cluster, let's go for more\n",
    "            #HDouuSet = set(HDunitList[t]).intersection(set(OUUc))  #the subset of the OUU cluster that's in this HD.  Defined above\n",
    "            HDouuCpop = np.sum([unitPop[u] for u in HDouuSet])\n",
    "            giveUpOnShedding = False            \n",
    "            shedCandidates, shedScores = list(), list()\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if HDouuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward far, overused\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            while HDouuCpop < maxExchangePop and not giveUpOnShedding:\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                    if contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDouuCpop += unitPop[shedU]\n",
    "                        #print(\"shedding unit\",shedU,\"from HD\",t,\"total shed Pop now\", HDouuCpop)\n",
    "                        HDouuSet.add(shedU)\n",
    "                        trySet.remove(shedU)\n",
    "                        del shedScores[shedCandidates.index(shedU)]\n",
    "                        del shedCandidates[shedCandidates.index(shedU)]\n",
    "                        newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                        for u in newSheddables:\n",
    "                            if HDouuCpop + unitPop[u] <= maxExchangePop and u not in shedCandidates:\n",
    "                                shedCandidates.append(u)\n",
    "                                shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            shedSet = HDouuSet.copy()  #set(OUUc).intersection(set(HDunitList[t]))\n",
    "            trySet = set(HDunitList[t]).difference(shedSet) \n",
    "            gap = aDP - np.sum([unitPop[u] for u in trySet])\n",
    "            nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "            for u in trySet:\n",
    "                for uu in unitNbrs[u]:\n",
    "                    if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:\n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append(5.*(unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                        #bias toward UNDERUSED, close\n",
    "            addedSet = set( )    \n",
    "            stillGoing = True \n",
    "            while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "                nearHDscore2 = nearHDscore.copy()\n",
    "                for ij, uu in enumerate(nearHDlist):\n",
    "                    nHDnbrs = len( set(unitNbrs[uu]).intersection(trySet) )\n",
    "                    if nHDnbrs == 1 :  #BIAS AGAINST CREATING A SLIM CHAIN\n",
    "                        nearHDscore2[ij] *= 0.5   #make this score closer to zero (less negative)  #NEW FOR GA 3/11/24\n",
    "                idx, ij, notYetPicked = np.argsort(nearHDscore2), 0, True   #originally, used nearHDscore itself here\n",
    "                while ij < len(nearHDscore) and notYetPicked:        \n",
    "                    listNo = idx[ij]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                    unitNoToAdd = nearHDlist[listNo]  #add this unit ...                            \n",
    "                    canAdd  = wontEnclave(unitNoToAdd, list(trySet), unitNbrs, borderUnits)\n",
    "                    if canAdd:\n",
    "                        notYetPicked = False\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    stillGoing = False  #can't add any more units; we can't without creating an enclave\n",
    "                else:\n",
    "                    gap -= unitPop[unitNoToAdd]\n",
    "                    addedSet.add(unitNoToAdd)\n",
    "                    trySet.add( unitNoToAdd)\n",
    "                    for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                        if uu not in trySet and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:  \n",
    "                            nearHDlist.append(uu)\n",
    "                            nearHDscore.append(5.* (unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] ) \n",
    "                            #bias toward UNDERUSED, ~close\n",
    "                    del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                    del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                    for ijj, uu in enumerate(nearHDlist.copy()):\n",
    "                        if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                            del nearHDscore[nearHDlist.index(uu)]\n",
    "                            del nearHDlist[ nearHDlist.index(uu)]\n",
    "            currPop = np.sum([unitPop[u] for u in trySet])\n",
    "            if abs(currPop - aDP) <= 2.* maxGap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addedSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t]    = currPop\n",
    "                nPatchSuccess +=1\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "        else:\n",
    "            nCouldntStart +=1\n",
    "            #print(\"Due to discontiguity, can't drop OUU cluster for HD, OUU\", t, OUU)\n",
    "    print(\"all done trying to reduce usage of unit\",OUU,\"final usage =\",r5(unitUse[OUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )\n",
    "            \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "cb60da52-ca13-45dd-8bf0-475213183a0d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "overused units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "underused units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"overused units\")\n",
    "maxPlot = 1.1\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > maxPlot:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(round(unitUse[u],2),unitGeom[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"underused units\")\n",
    "minPlot = 0.86\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minPlot:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(round(unitUse[u],2),unitGeom[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa912158-e8e9-46f3-8c3d-f6b17c26e62c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Note - for MA, above is second run-through after overuse, then underuse pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "0d4f1372-6044-4803-91d9-de4b95a40083",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 0.99997 1.00015 and their SDs are 0.1718 0.05987\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 2157\n",
      "working on HD 300 out of 2157\n",
      "working on HD 600 out of 2157\n",
      "working on HD 900 out of 2157\n",
      "working on HD 1200 out of 2157\n",
      "working on HD 1500 out of 2157\n",
      "working on HD 1800 out of 2157\n",
      "working on HD 2100 out of 2157\n",
      "all done checking HD and complement contiguity for all 2157 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "ac9cd133-94f0-494b-ac74-0c584482d97e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets write these UNIT lists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Lets write these UNIT lists to a file\")\n",
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"fullyPatched.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26b87c95-101f-4f8f-bb73-feb1827ac6b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "noFrag = int(input(\"enter 1 if there were NO fragmented VTDs\"))\n",
    "if noFrag == 1:\n",
    "    nFragmentedVTDs,fragmentedVTDs = 0, list()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "ec102553-e4f3-4010-86bc-725a01b95788",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter([v for v in range(len(parentVTDno))],parentVTDno)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "55d64c64-b473-49d0-b863-d4123cbb9b6c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for v in range(nVTDs):\n",
    "    if MAP.contains(vtdGeom[v].centroid) and v not in parentVTDno:\n",
    "        print(v,\"is in the map but not in the parent vtd list\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "ecd50c96-51ce-4d83-b0df-e8c645c35815",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after above patching, write these contiguous lists to a file, converting to vtd lists\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if there were NO fragmented VTDs 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on full or partial VTD master list for unit 0\n",
      "Now converting unit lists to vtd lists for all HDs\n"
     ]
    }
   ],
   "source": [
    "#THIS WORKS FOR BOTH VTD- AND UNIT-BASED (FRAG VTD) -- **NOTE: DOES NOT ADD IN CUT DISTRICTS AND REBALANCE WEIGHTS\n",
    "print(\"after above patching, write these contiguous lists to a file, converting to vtd lists\")\n",
    "noFrag = int(input(\"enter 1 if there were NO fragmented VTDs\"))  #for simple states where units are at least whole vtd's\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist, unitFragVTDlist, unitVTDfrac = [list() for u in range(nUnits)], [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "unitFullVTDlist, unitPartialVTDlist =       [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "\n",
    "for u in range(nUnits):\n",
    "    if u %2000 == 0:\n",
    "        print(\"working on full or partial VTD master list for unit\",u)\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "        unitFullVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "            unitFullVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        if noFrag == 1:\n",
    "            unitVTDlist[u].append(allUnits[u] )\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitVTDlist[u].append(surroundedVTDs[i])\n",
    "        else:\n",
    "            unitFragVTDlist[u].append(allUnits[u])\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitFragVTDlist[u].append(surroundedVTDs[i])\n",
    "            vtdFrac = [0. for V in range(nVTDs)]\n",
    "            for vv in unitFragVTDlist[u]:\n",
    "                V = parentVTDno[vv]\n",
    "                if len(VTDchildren[V]) == 1:  #nonfragmented vtd\n",
    "                    vtdFrac[V] = 1.\n",
    "                else:\n",
    "                    if tractPop[V] > 0:\n",
    "                        vtdFrac[V] += fragVTDgeom[vv].area / vtdGeom[V].area #fragVTDpop[uu] / tractPop[v]  #we overwrote the frag pops earlier; can't use\n",
    "            for v in range(nVTDs):\n",
    "                if vtdFrac[v] > 0.0001:\n",
    "                    if vtdFrac[v] > 0.999:\n",
    "                        unitFullVTDlist[u].append(v)\n",
    "                    else:\n",
    "                        unitPartialVTDlist[u].append(v)\n",
    "                        unitVTDfrac[u].append(vtdFrac[v] )\n",
    "                                    \n",
    "HDpartialVTDlist, HDfullVTDlist, HDvtdFrac = [list() for t in range(nHDs)] ,[list() for t in range(nHDs)],[list() for t in range(nHDs)]               \n",
    "print(\"Now converting unit lists to vtd lists for all HDs\")\n",
    "if noFrag == 1:\n",
    "    for t in popHDlist:\n",
    "        for u in HDunitList[t]:\n",
    "            vtdList[t] += unitVTDlist[u]\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "else:\n",
    "    for t in popHDlist:\n",
    "        partialList, partialFrac = list(), list()  #for many HDs, we'll recover whole VTDs when aggregating units\n",
    "        for u in HDunitList[t]:\n",
    "            HDfullVTDlist[t] +=   unitFullVTDlist[u] \n",
    "            partialList +=         unitPartialVTDlist[u]\n",
    "            partialFrac +=          unitVTDfrac[u]\n",
    "        partialSet = set(partialList)  #non-duplicated set of partials across the HD, prepping to aggregate where possible\n",
    "        partialSetFrac, partialSetList = [0. for v in partialSet], list(partialSet)\n",
    "        for i,v in enumerate(partialList):\n",
    "            partialSetFrac[partialSetList.index(v)] += partialFrac[i]\n",
    "        for i,v in enumerate(partialSetList):\n",
    "            if partialSetFrac[i] > 0.999:  #the district has all the fragments of the original VTD contained in the HD's units\n",
    "                HDfullVTDlist[t].append(v)\n",
    "            else:\n",
    "                HDpartialVTDlist[t].append(v)\n",
    "                HDvtdFrac[t].append(      r5(partialSetFrac[i]) )  #save the aggregated fraction of this VTD across all units\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"patchedVTDs.csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "5075814f-4a75-4807-ad8d-b3cc969cb470",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7211 7213 7211 7211 7211 7211\n"
     ]
    }
   ],
   "source": [
    "\n",
    "HDweight = HDweight[0:nHDs]\n",
    "HDvPop = HDvPop[0:nHDs]\n",
    "HDweight = HDweight[0:nHDs]\n",
    "HDfullVTDlist = HDfullVTDlist[0:nHDs]\n",
    "HDpartialVTDlist = HDpartialVTDlist[0:nHDs]\n",
    "HDvtdFrac = HDvtdFrac[0:nHDs]\n",
    "hdCPx, hdCPy = hdCPx[0:nHDs], hdCPy[0:nHDs]\n",
    "print(nHDs,len(tList), len(HDweight), len(HDvPop), len(HDvtdFrac), len(hdCPx) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "e385f357-5b7e-46f5-b054-c5c38d799395",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999999999999055 2157\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(np.sum(HDweight), nHDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "8d922813-2854-41d7-9405-22db5d5d5a59",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now append the cut districts and adjust the HDweights\n",
      "8 1 are the number of HD-drawn and cut districts\n"
     ]
    }
   ],
   "source": [
    "print(\"Now append the cut districts and adjust the HDweights\")\n",
    "print(nDistricts,nCutDistricts,\"are the number of HD-drawn and cut districts\")\n",
    "HDweight = [nDistricts/float(nCutDistricts + nDistricts) * HDweight[t] for t in range(nHDs)]\n",
    "tList = [t for t in range(nHDs)]\n",
    "if type(hdCPx) != type(list()):\n",
    "    hdCPx, hdCPy = hdCPx.to_list(), hdCPy.to_list()\n",
    "for L in allCutLists:\n",
    "    tList.append(len(tList))\n",
    "    HDweight.append(1./(nCutDistricts + nDistricts))\n",
    "    pop = np.sum([tractPop[v] for v in L])\n",
    "    HDvPop.append(pop )\n",
    "    HDfullVTDlist.append(L)\n",
    "    HDpartialVTDlist.append(list() )\n",
    "    HDvtdFrac.append(list() )\n",
    "    hdCPx.append(np.sum([tractPop[v]*tractCPx[v] for v in L]) / pop )\n",
    "    hdCPy.append(np.sum([tractPop[v]*tractCPy[v] for v in L]) / pop )\n",
    "    \n",
    "\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                        \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"patchedWithCutsVTDs.csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "b0b611f7-6c35-495d-b38c-c848317d63ab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999999999999055\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(HDweight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "b9d808de-119f-4197-8e0c-8407c105d789",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vtdUSE = [0. for v in range(nVTDs)]\n",
    "for u in range(nUnits):\n",
    "    for v in unitFullVTDlist[u]:\n",
    "        vtdUSE[v] += 1\n",
    "    for i,v in enumerate(unitPartialVTDlist[u]):\n",
    "        vtdUSE[v] += unitVTDfrac[u][i]\n",
    "\n",
    "plt.scatter([v for v in range(nVTDs)], vtdUSE)\n",
    "plt.show()\n",
    "unused = list()\n",
    "for v in range(nVTDs):\n",
    "    if vtdUSE[v] < 0.2 and MAP.contains(vtdGeom[v].centroid) and tractPop[v] > 0:\n",
    "        unused.append(v)\n",
    "        #print(v,\"in county\",countyNo[v],\"has pop\",tractPop[v],\"and map-total usage of\",vtdUSE[v],\"its surroundedness is\",v in surroundedVTDs)\n",
    "        plotPoly(vtdGeom[v].centroid.buffer(0.1))\n",
    "        plotCenter(v,vtdGeom[v],8)\n",
    "    #if vtdUSE[v] > 0.2 and vtdUSE[v] < 0.95:\n",
    "    #    plotCenter(r3(vtdUSE[v]),vtdGeom[v])\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "3d2a393c-5ab8-4140-80eb-1153c2ef41f9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in range(nUnits):\n",
    "    if unitUse[u]  < 0.9:\n",
    "        plotPoly(unitGeom[u],0.2)\n",
    "        plotCenter(r3(unitUse[u]),unitGeom[u],6)\n",
    "    if  unitUse[u]  > 1.1:\n",
    "        plotPoly(unitGeom[u],1.2)\n",
    "        plotCenter(r3(unitUse[u]),unitGeom[u],6)\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "404935e6-f0de-4b51-9db9-020aa208f852",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "integrity check: vtd-based pops\n",
      "x = n surrounded, y= unit - vtd-based\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"integrity check: vtd-based pops\")\n",
    "HDvtdbasedPop = [0. for t in range(nHDs)]\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "nSurrs = [0. for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "        if u in surroundedVTDs:\n",
    "            nSurrs[t] += 1\n",
    "    for v in HDfullVTDlist[t]:\n",
    "        HDvtdbasedPop[t] += origVTDpop[v] #tractPop[v]\n",
    "    for i,v in enumerate(HDpartialVTDlist[t]):\n",
    "        HDvtdbasedPop[t] += HDvtdFrac[t][i] * origVTDpop[v] #tractPop[v] also works\n",
    "        \n",
    "plt.scatter([nSurrs[t] for t in popHDlist], [HDvPop[t] - HDvtdbasedPop[t] for t in popHDlist] )\n",
    "print(\"x = n surrounded, y= unit - vtd-based\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef27af54-daf5-4e8f-9230-bce5b0b1e705",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "3ad3342b-b8e3-46f5-a3e5-f49df386b88f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\n",
      "Let's read in the 2020 and 2016 voting data for MA\n",
      "In year/election 2020 Dem and GOP total votes were 2382183 1167184 for a stateGOP of 0.32884\n",
      "There are a total of 2157  vote-mapped voting districts from election(s) in year  2020\n",
      "In year/election 2016 Dem and GOP total votes were 1995182 1090877 for a stateGOP of 0.35349\n",
      "There are a total of 2157  vote-mapped voting districts from election(s) in year  2016\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\")\n",
    "#note: we started w possibility of separate election --> 2020 vtd files by year.  Now we use ALARM combined 2016+2020 --> 2020 csv's for all exc CA\n",
    "# copied from \"HDpartisanAnalysis\"\n",
    "print(\"Let's read in the 2020 and 2016 voting data for\",STATE)\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G16PREDCLI\"], [\"G16PRERTRU\" ], list()\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G20PREDBID\", \"G16PREDCli\"], [\"G20PRERTRU\" ,\"G16PRERTru\" ], list()\n",
    "DemCandidates, RepCandidates, vestDFs = [\"pre_20_dem_bid\", \"pre_16_dem_cli\"], [\"pre_20_rep_tru\" ,\"pre_16_rep_tru\" ], list()\n",
    "nYears = 2\n",
    "unitIDs, vestReps,vestDems,yearLean = [list()]*nYears, [0]*nYears, [0]*nYears, [0.5]*nYears\n",
    "years = [str(2020),str(2016)]\n",
    "DemVotes, RepVotes = [list() for y in years], [list() for y in years]\n",
    "vestDir = \"./state_map_files/\" + STATE.lower()\n",
    "vestDF = pd.read_csv(vestDir + \"_2020_vtd.csv\")\n",
    "mergedDF = vestDF.copy() #pd.merge(mergedDF,vestDF, on = \"GEOID20\")\n",
    "for y,year in enumerate(years):\n",
    "    DemVotes[y], RepVotes[y], unitIDs[y] = mergedDF[DemCandidates[y]], mergedDF[RepCandidates[y]], vestDF[(\"GEOID20\")]\n",
    "    vestDems[y], vestReps[y] = np.sum(DemVotes[y]), np.sum(RepVotes[y])\n",
    "    yearLean[y] = vestReps[y]/float(vestDems[y]+vestReps[y])\n",
    "    print(\"In year/election\",year,\"Dem and GOP total votes were\",int(vestDems[y]),int(vestReps[y]),\"for a stateGOP of\",r5(yearLean[y]) )\n",
    "    nVTDs = len(DemVotes[y])\n",
    "    print(\"There are a total of\",nVTDs,\" vote-mapped voting districts from election(s) in year \",years[y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f628069d-4177-401e-bf49-b7f194d0ef8b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "5cf73783-4a48-4a3f-a2e3-c1b9ff32c859",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now let's read in our ensemble of HD districts for MA\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the filename with the vtd assignments to districts; e.g. MO1654contigPatchBoth.csv MA2157patchedWithCutsVTDs.csv\n",
      "enter the number of HD-drawn districts in the state; e.g. 8 9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This ensemble or enacted map describes 2158 drawn districts.  This state has 9 districts per map\n",
      "converting vtd list strings to vtd lists by drawn district ...\n",
      "the total weight across all rows should be 1.000, actually is 1.0\n",
      "I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\n",
      "translating from HD order of precinct rows to VEST order\n"
     ]
    }
   ],
   "source": [
    "print(\"Now let's read in our ensemble of HD districts for\",STATE)\n",
    "infilename = input(\"enter the filename with the vtd assignments to districts; e.g. MO1654contigPatchBoth.csv\")\n",
    "\n",
    "HDdf = pd.read_csv(\"2024state_HD_output/\"+infilename)\n",
    "nRows = len(HDdf)\n",
    "nStateDistricts = int(input(\"enter the number of HD-drawn districts in the state; e.g. 8\"))\n",
    "print(\"This ensemble or enacted map describes\",nRows,\"drawn districts.  This state has\",nStateDistricts,\"districts per map\")\n",
    "\n",
    "HDvPop = HDdf[\"HDvPop\"].to_list()\n",
    "HDweight = HDdf['HDweight'].to_list()\n",
    "#tractPop = HDdf['tractPop'].to_list()\n",
    "#statePop = np.sum(tractPop)\n",
    "tractCPx = HDdf['centroid x'].to_list()\n",
    "tractCPy = HDdf['centroid y'].to_list()\n",
    "HDvtdListString = HDdf[\"HDvtdList\"]\n",
    "print(\"converting vtd list strings to vtd lists by drawn district ...\")\n",
    "HDvtdList = [list() for t in range(nRows) ]\n",
    "for t in range(nRows):\n",
    "    if HDvtdListString[t] != \"[]\":\n",
    "        HDvtdList[t] = ast.literal_eval(HDvtdListString[t])\n",
    "\n",
    "if \"partials\" in HDdf.columns.values: #\"splitTractNo\" #.to_list():\n",
    "    splitTractList, splitTractUseList = HDdf['partials'], HDdf['HDvtdFrac']\n",
    "    splitTractNo = [ast.literal_eval(splitTractList[t])    for t in range(nRows)]\n",
    "    splitTractUse = [ast.literal_eval(splitTractUseList[t]) for t in range(nRows)]\n",
    "else :  #incoming file didn't use any partial units, only whole ones\n",
    "    splitTractNo, splitTractUse = [[] for t in range(nRows)] , [ [] for t in range(nRows)]\n",
    "\n",
    "print(\"the total weight across all rows should be 1.000, actually is\",r5(np.sum(HDweight)) )\n",
    "print(\"I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\")\n",
    "censusGEOID20 = tractPopFile['GEOID20']\n",
    "miniHDdf = pd.DataFrame( {\"GEOID20\":censusGEOID20} )\n",
    "vestNo = [v for v in range(nVTDs)]\n",
    "print(\"translating from HD order of precinct rows to VEST order\")\n",
    "miniVestDF = pd.DataFrame( {\"GEOID20\":mergedDF['GEOID20'], \"vestNo\":vestNo} )\n",
    "mappingDF = pd.merge(miniHDdf,miniVestDF,on=\"GEOID20\")\n",
    "vestID = mappingDF['vestNo']  #this maps each named unit in a HDVL to the correct vestNo row with voting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9c38b293-7a46-4106-a972-b4868206bc2d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "540c6d1d-7728-48bf-90f0-f124b25ac7c2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sanity check - Rep-leaning vtds for year 2020\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"sanity check - Rep-leaning vtds for year\",years[0])  #for MA, do GOP-leaning\n",
    "for v in range(nVTDs):\n",
    "    plotPoly(vtdGeom[v],0.2)\n",
    "    if DemVotes[0][vestID[v]] < RepVotes[0][vestID[v]]:\n",
    "        plt.text(vtdGeom[v].centroid.x, vtdGeom[v].centroid.y, \"R\", color = \"red\",fontsize=6)\n",
    "        #plotCenter(\"d\",vtdGeom[v],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "56ed7e9d-1400-4848-892e-737555552d58",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\n",
      "working on drawn district no 0\n",
      "working on drawn district no 1000\n",
      "working on drawn district no 2000\n",
      "here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\n",
      "true 2020: 2382183 1167184 -1214998\n",
      "ensemble : 2388052 1158521 -1229531\n",
      "the true and ensemble state GOP leans are 0.32884 0.32665932134703696\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\")\n",
    "nDrawnDistricts = len(HDweight)  #now including cut districts\n",
    "nMapDistricts = nCutDistricts + nDistricts\n",
    "nEnsembleDems, nEnsembleReps = [0.]*nYears, [0.]*nYears\n",
    "y=0\n",
    "for t in range(nDrawnDistricts):\n",
    "    if t%1000 == 0:\n",
    "        print(\"working on drawn district no\",t)\n",
    "    nEnsembleDems[y] += nMapDistricts* HDweight[t] * ( np.sum([DemVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([DemVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "    nEnsembleReps[y] += nMapDistricts* HDweight[t] * ( np.sum([RepVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([RepVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "\n",
    "print(\"here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\")\n",
    "print(\"true 2020:\",int(vestDems[y]),int(vestReps[y]),              int(vestReps[y] - vestDems[y]) )\n",
    "print(\"ensemble :\",int(nEnsembleDems[y]),int(nEnsembleReps[y]),    int(nEnsembleReps[y] - nEnsembleDems[y]) )\n",
    "print(\"the true and ensemble state GOP leans are\",r5(vestReps[y]/(vestReps[y]+vestDems[y])),\n",
    "      nEnsembleReps[y]/(nEnsembleReps[y]+nEnsembleDems[y]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "5c607c63-df0d-4334-8131-368c090c76d2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total drawn districts, HD-drawn, map districts 2158 2157 9\n"
     ]
    }
   ],
   "source": [
    "print(\"total drawn districts, HD-drawn, map districts\",nDrawnDistricts,nHDs, nMapDistricts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb0d0d12-6bf4-4d5f-9236-fa8735393e98",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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